Prince Singh

u/princesinghdev • Founding Engineer & AI Architect

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Delhi, India preparationstreet.com Building since 2020

Full-Stack • System Design • Cloud & DevOps • Microservices • High-Performance APIs • Scalable Infrastructure

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Posted by princesinghdev

Building production systems, tuned to the last millisecond & dollar.

Intro

From architecture to monitoring - I build systems that scale.

100,000+

Community Reach

40,000+

Engineers Mentored

Systems Shipped

2020

Building Since

5000+

Problems solved across 6 tracked platforms

Systems shipped & live

HackerEarthHackerRankInterviewBitGFGLeetCodeCodeStudio

40,000+ engineers mentored · 100,000+ total community reach

I’ve engineered the core of our AI ecosystem Multi LLM Orchestration, ARX (AI Architect), Versus, Voice with 24+ Brains, AskDSA, RoadmapAI, CodeLLM, AskAI, AI Code Editor, and the Global AI Search, designing end-to-end Agentic AI pipelines with RAG-driven personalization, MCP-layered orchestration, and multi-model LLM architectures that deliver real-time learning guidance, deterministic code evaluation, and deeply context-aware programming assistance at scale.

I’ve worked hands-on with leading LLM MODELS and AI platforms including OpenAI, Google AI, Anthropic (Claude), MistralAI, Meta, Grok, Moonshot and Databricks, implementing intelligent model routing, fallback strategies, cost-aware inference, and latency-optimized multi-provider execution.

The system leverages Vector Databases (ChromaDB) for semantic context retrieval and long-term memory, enabling high-precision RAG workflows. I’ve implemented token-level streaming responses to deliver real-time AI output, along with response caching, embedding reuse, and prompt-result memoization to significantly reduce latency, repeated inference, and overall token costs.

My work spans LLM System Design, tokenization & reasoning flows, streaming & tool-calling agents, vectorized context pipelines, and high-availability AI microservices, forming the intelligence backbone of the platform.

I'm a Founding Engineer & AI Architect with 2.5 years of hands-on experience building large-scale AI systems, distributed backend infrastructures, and production-grade full-stack platforms. At ProPeers, I own and engineer the core systems that power 80%+ of total platform traffic including Roadmaps, RoadmapAI, AskAI, CodeLLM, Global AI Search, and the Contextual AI Code Editor.

I design high-scale backend architectures, real-time data pipelines, aggregation engines for 600K+ users, Redis-backed caching layers, search-validation systems, role-based access flows, rate-limiting frameworks, and CI/CD deployment automation that cut release time by 34% and improved reliability across 150+ microservices. Through SSR, dynamic imports and hybrid rendering patterns, I’ve reduced key user journey response times from 1.1s → 200ms, delivering a noticeably smoother product experience.

As an AI Architect, I build Agentic AI pipelines, RAG retrieval systems, MCP protocol layers, and multi-model inference workflows using Azure OpenAI, Azure Databricks, GPT models, and Llama 3.x OSS models. My work spans token optimization, context-window compression, semantic chunking, and adaptive prompt engineering to deliver intelligent experiences at <1s latency under real production traffic.

I’ve engineered RoadmapAI with a self-learning RAG pipeline (text-embedding-ada-002, ChromaDB, semantic filters, vector enrichment), achieving ~99% roadmap accuracy and lifting roadmap ratings from the early 12% baseline. I built CodeLLM, a production AI judge featuring multi-language detection, dual-layer JSON parsing, COMPILATION/RUNTIME/VALIDATION error classification, and deterministic verdict synthesis for educational code evaluation.

I developed AskAI with MCP-layered prompts, resource-type detection (roadmap/article/practice), O1/O3 model routing, token metering, and auto-structured responses improving resolution speed 2× and engagement 3×. I also built the AI Code Editor with ~40ms inference, inline reasoning, multi-language execution, and deep integration with RoadmapAI and CodeLLM, significantly boosting editor retention.

Beyond AI flows, I’ve implemented token-based tiered access systems (one-time/monthly/yearly) on top of these capabilities, and engineered self-optimizing RAG pipelines and distributed multi-model inference workflows that balance accuracy, cost and latency under real-world traffic.

On the product and platform side, I’ve delivered Individual Roadmap Communities, scalable live-stream pipelines, error-resilient API layers, multi-step onboarding flows, connected roadmap progress engines, and search validation systems ensuring hallucination-free retrieval across Roadmaps and RoadmapAI.

At the infrastructure layer, I’ve reduced downtime by 90% (4 hours → 45 mins/month), stabilized Azure VM workloads, eliminated Bastion and high-cost D8 VM footprints, fixed bandwidth cost spikes, and built high-availability fallback layers with cache-first routing and distributed failover.

Day-to-day, I work across MERN + TypeScript, Node.js microservices, Docker/Kubernetes, Azure Cloud, Databricks, CI/CD automation, Prometheus/Grafana observability, and async caching pipelines powering 100K+ monthly active operations.

Outside core engineering, I’m a Problem-Solving & DSA Enthusiast with 5000+ problems solved, a 1700+ day coding streak, and top 0.1% global rankings across platforms. As a mentor to 40,000+ learners, I help engineers master DSA, System Design, Development, DevOps, and Remote Job Preparation, guiding them from theory to real-world success.

I love building scalable systems, intelligent architectures, and next-generation AI-first engineering experiences that blend reliability, performance, and deep technical innovation.

6 Layer Multi RAG Architecture1 / 2

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Posted by princesinghdev

From intern to Founding Engineer - 3 roles across 2 companies

Career

Where I've worked and what actually shipped there.

Companies

Roles

July 2025 – Present

Founding Engineer & AI Architect

Current

ProPeers · July 2025 – Present · Delhi, India · Remote

Architected the full AI ecosystem powering RoadmapAI, CodeLLM, AskAI, Global AI Search and the AI Code Editor building Agentic AI pipelines, RAG systems, MCP server architecture and LLM orchestration that now drives 80%+ of total platform traffic.
Engineered RoadmapAI end-to-end with a self-learning RAG pipeline (text-embedding-ada-002, ChromaDB, semantic filtering, adaptive difficulty) and MCP-layered prompts, achieving sub-second inference and large-scale personalization.
Delivered ~99% personalized roadmap accuracy using Agentic flows, structured prompt masks, multi-model routing, and RAG optimization directly improving RoadmapAI user ratings from the early 12% baseline.
Built CodeLLM, an AI judge with multi-language detection, dual-layer JSON parsing, context-aware error classification (COMPILATION/RUNTIME/VALIDATION), semantic retrieval and deterministic verdict synthesis.
Developed AskAI, an agentic programming assistant using MCP-based prompt pipelines, resource-aware context analysis, dynamic O3Mini/O1 routing, token metering and automated formatting boosting engagement 3× and answer resolution speed 2×.
Shipped the AI Code Editor with real-time AI review (<40ms), inline reasoning, multi-language execution and deep RoadmapAI/CodeLLM integration raising editor retention by 40%.
Scaled Roadmap features to 120K+ organic users and improved MAU by 46% through rapid iteration, tight user-feedback loops and stable AI feature launches.
Delivered Individual Roadmap Communities enabling peer-matching, shared progress tracking and roadmap-level micro-communities.
Optimized CI/CD and deployment systems, cutting deployment time by 34%, automating multi-service rollouts, and enabling safer high-frequency releases.
Reduced platform downtime by 90% (4 hrs to 45 mins/month) via infra hardening, progressive fallbacks, cache-first routing, real-time health checks and load-aware autoscaling.
Implemented complete analytics & aggregation pipelines for 600K+ users with Redis caching, chunked batch aggregation, API acceleration and advanced rate-limit enforcement.
Developed full search-validation engines (Roadmaps + RoadmapAI), ensuring context-safe retrieval, hallucination-resistance and consistent multi-node semantic validation.
Performed Azure cost & infra optimization VM right-sizing, eliminated Bastion, stabilized Redis/Entra costs, contained Cognitive Service spikes and resolved large bandwidth egress surges.

ProPeers · July 2024 – July 2025 · Delhi, India · Remote

Built and scaled the flagship "Roadmaps" feature, delivering 100+ curated learning paths across DSA, Development, and System Design used by 600K+ users. Improved personalization and relevance, while reducing API response time from 2.1s to < 300ms, resulting in a 7x faster experience and 40% higher user engagement.
Worked on complex APIs to reduce processing time and improved tab switching experience for smoother navigation
Developed and integrated the "AskAI + Discussion Forum", an intelligent peer-programming assistant where users can interact with AI to solve DSA/Dev doubts and collaborate with others enabling on-demand doubt resolution and community learning.
Engineered a Session Recording Bot using Python, Selenium, and headless Azure VMs with deep link automation automating session joining and recording, cutting down 100% of manual effort and improving reliability.
Optimized 150+ APIs by implementing advanced caching layers, async processing, and API pipelines, reducing backend latency by up to 70% and improving system throughput.
Reduced core web vitals TBT, LCP, and FCP from 4.4s to 990ms through advanced frontend optimizations (SSR, dynamic imports, lazy-loading APIs), significantly boosting UX for 15K+ monthly active users.
Led the end-to-end performance overhaul of the platform, focusing on smoother tab-switching experiences, minimal downtime, and blazing-fast navigation across the app.
Migrated MongoDB from Atlas to self-hosted replica sets, wrote automated backup & recovery scripts, set up VMs, and integrated cron-based backups to Azure Blob, ensuring data durability and cost-efficiency.
Set up real-time monitoring and alerting with Prometheus and Grafana, ensuring system health, proactive issue resolution, and enhanced DevOps visibility.
Deployed scalable CI/CD pipelines using Azure, GitLab, and Vercel, ensuring zero-downtime deployments and faster iteration cycles across teams.
Handled end-to-end production deployment and scaling for a system serving 15K+ users, maintaining high availability, fault tolerance, and robust performance at scale.

Cloud Conduction · Jan 2024 – June 2024 · USA, · Remote

Built an AI-powered chat application from the ground up using React and .NET, improving frontend efficiency by 60% and backend performance by 30%, delivering a highly responsive user experience.
Integrated and optimized AI model responses, reducing latency from 1.86s to 1.2s (35% faster) through strategic API design, caching, and performance tuning.
Designed scalable cloud architecture on Microsoft Azure for AI workloads, improving system throughput by 10% while significantly reducing infrastructure costs via autoscaling and resource optimization.
Developed modern, responsive UI components in React that improved user engagement metrics by 25%, including better retention and interaction rates.
Implemented secure, scalable API gateways in .NET Core, capable of handling 500+ concurrent requests with 99.9% uptime, supporting production-level reliability.
Led the implementation of new features using the MERN stack, cutting down development time by 40%, and accelerating product iteration cycles.
Established CI/CD pipelines (Azure DevOps & GitHub Actions), reducing deployment failures by 75% and enabling faster, automated releases.
Conducted in-depth code reviews and optimization, reducing technical debt by 30%, standardizing best practices across teams, and improving maintainability.
Owned and managed the complete project lifecycle, from initial system design and dev planning to production deployment, server setup, and post-launch support.

Posted by princesinghdev

Impactful work as an individual contributor

Product

The AI ecosystem and infrastructure I own end-to-end.

Systems Owned

Technologies

Live in Production

Led a platform-wide FinOps and infrastructure optimization initiative across the full Azure + AWS footprint - auditing every VM, disk, managed service, and AI workload against real utilization data - cutting monthly cloud spend from ₹222K to ₹185K (17% reduction, ₹37K/month saved) with an active optimization roadmap targeting ₹150K/month (~32% total reduction) - all with zero performance regression and zero downtime for production traffic.
Executed a utilization-driven VM right-sizing program across 10+ virtual machines: analyzed CPU, memory, and IOPS metrics per workload over sustained windows, downgraded over-provisioned instances that were burning budget at single-digit utilization, and upgraded genuinely constrained VMs where resource pressure was hurting latency - sizing every machine to its measured requirement instead of its original guess.
Hunted down and decommissioned every source of orphaned spend: unwanted mounted VMs, unused servers, and deactivated services were fully deallocated and removed from resources completely - including their attached disks, static IPs, and network interfaces - so nothing invisible kept billing after the workload it served was gone.
Implemented workload-aware storage tiering across managed disks: Premium SSDs retained only where IOPS/latency actually demand them (database and hot-path workloads), while logs, backups, and low-IO volumes were downgraded from Premium to Standard SSD - and under-provisioned hot disks upgraded - aligning storage cost precisely with each workload's real I/O profile.
Optimized managed service footprints: right-sized and consolidated email services and Redis instances - tier corrections, connection pooling, and TTL/eviction policy tuning - so supporting services stopped running at capacity tiers the actual traffic never justified.
Enforced AI cost governance at the platform level: introduced usage limits on AI infrastructure and AI services (per-user rate limits capping worst-case inference spend) and optimized context-window and token usage - trimming prompt payloads, capping response windows, and reusing cached responses - so AI cost per request dropped without degrading answer quality, and total AI spend became a bounded, predictable line item instead of an open-ended one.
Eliminated structurally expensive footprint items: removed the Azure Bastion dependency and high-cost D8 VM instances, and fixed bandwidth cost spikes at the routing/caching layer - attacking the largest single line items on the bill first for maximum savings per change.
Drove every decision from measured data, not assumptions: utilization monitoring and cost-analysis dashboards per resource group, budget alerts on anomalous spend, and a monthly cost-review loop that treats the cloud bill as an engineering metric - which is how the ₹185K result keeps compounding toward the ₹150K target instead of creeping back up.
Executed all resizes, tier migrations, and decommissions with production-safe change discipline: changes scheduled in low-traffic windows, health checks validating each workload after every resize, and rollback paths held ready - the entire optimization program shipped without a single user-facing incident.

System Architecture & Details

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Architected an end-to-end RAG-powered AI learning platform serving 600K+ users with sub-second inference latency, leveraging Azure OpenAI embeddings (text-embedding-ada-002), ChromaDB vector indexing, and semantic retrieval with dynamic topic-aware filtering achieving 0.25 similarity-threshold precision
Engineered a self-evolving knowledge graph where every AI-generated artifact (roadmaps, articles, practice questions) is automatically embedded, vectorized, and reintegrated into ChromaDB creating a continuously learning retrieval layer that improves semantic accuracy with each user interaction
Built an intelligent RAG pipeline with multi-stage context optimization combining semantic vector similarity search, domain-specific keyword enforcement, exclusion-based noise filtering, and quality-threshold gating (0.25 cutoff) to deliver hallucination-resistant contextual augmentation
Designed a production-grade MCP-compliant prompt orchestration system with structured message arrays (system/user roles), dynamic context injection based on user proficiency levels (1-5 scale), adaptive difficulty mapping (Beginner/Intermediate/Advanced), and goal-oriented content generation across 3 formats
Implemented a real-time intent classification engine with confidence-weighted pattern matching across 4 transformation operators (NEW_SUBROADMAP, ADD_TOPICS, PROJECT_CREATION, REGENERATE_PIPELINE) using 20+ keyword signatures per intent and hierarchical fallback resolution for ambiguous requests
Developed a conflict-safe progress-preserving merge algorithm that maintains atomic user state (isDone flags, bookmarks, annotations, code links) during AI-driven content expansions through differential patching, duplicate detection, and rollback-capable database transactions
Created a multi-layer security validation framework with lexical abuse detection (violent/illegal/inappropriate patterns), technical relevance scoring across 15+ engineering domains, injection-attack guards, and AI-powered verification with a 0.6 confidence threshold for edge cases
Architected a scalable token-governance system with tiered allocation models (8 free tokens + purchased pools), operation-based cost accounting (Creation: 2 tokens, Customization: 4 tokens), atomic transaction handling via MongoDB optimistic locking, and graceful quota degradation
Optimized database performance through strategic indexing with compound indices on (userId, sessionId, isDeleted), aggregation pipeline optimization for history queries, session-based data isolation, soft-delete mechanisms, and pagination limiting to 50 records per fetch
Implemented a multi-model AI orchestration layer supporting dynamic routing between o3-mini (8K context window) for complex generation and gpt-3.5-turbo (4K context) for standard operations, with consistent MCP interface abstraction and model-specific parameter tuning
Built a resilient fallback architecture ensuring 100% availability with RAG-miss graceful degradation, sparse-query fallback prompts, cache-bypass recovery paths, multi-tier error handling, structured security-event logging, and health-check monitoring across all AI subsystems

System Architecture & Details

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Architected an end-to-end AI-powered code evaluation system replacing traditional compilers with RAG-enhanced logical judgment, leveraging semantic retrieval, model-context engineering, and multi-model orchestration to achieve 99% evaluation accuracy across Python, Java, C++, and JavaScript.
Built a multi-stage language detection engine using regex patterns, anti-pattern suppression, syntax heuristics, and confidence-based classification to prevent cross-language submissions and ensure evaluation integrity for every code block.
Implemented a production-grade MCP-compliant prompt pipeline generating strictly structured system/user message arrays, including judge instructions, evaluation rules, test-case schemas, complexity requirements, and JSON-first verdict formatting.
Designed a dual-layer response parsing system with JSON block extraction, Markdown fallback resolution, regex-based error isolation, and verdict normalization to guarantee consistent outputs even with noisy AI responses.
Engineered a multi-model AI orchestration layer dynamically routing requests between o3-mini (accuracy), o1 (reasoning), and gpt-35-turbo (performance) with token-window optimization and context-aware selection.
Integrated a RAG pipeline with ChromaDB using text-embedding-ada-002 to retrieve reference solutions, constraints, edge cases, and complexity hints, enabling AI to perform context-enriched evaluation rather than plain code matching.
Created a modular progress-tracking engine mapping submissions to TodoItems, Topics, and Subroadmaps, automatically updating isDone status and learning milestones through real-time backend sync and user completion logic.
Developed a robust validation and error-classification layer with strict checks for payload integrity, language mismatches, test-case correctness, sanitized code inspection, and COMPILATION_ERROR / RUNTIME_ERROR / VALIDATION_ERROR generation.
Implemented a structured verdict generator delivering human-like educational feedback including passed/failed test-case breakdowns, root-cause explanations, error localization, corrected code suggestions, and time/space complexity analysis.
Optimized backend infrastructure using MongoDB submission architecture with collections for Submission, TodoItem, Topic, UserTodoItemMapping, ensuring analytics-ready storage, high-throughput writes, and environment-aware routing for dev/prod deployments.
Achieved scalable, real-time evaluation flows combining JWT-secured endpoints, load-balanced AI calls, semantic retrieval augmentation, multi-model fail-safes, and a high-availability fallback pipeline for uninterrupted code judging.

System Architecture & Details

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Architected and developed a production-grade AI programming assistant handling 100+ RPS with 99.9% uptime across learning platform resources.
Engineered sophisticated multi-model AI orchestration routing questions between O3Mini, O1, GPT-3.5 Turbo, and Llama 3.3 based on question complexity and resource type.
Built comprehensive token management system with dual-token architecture (9 free + purchased), atomic MongoDB operations, and fair usage enforcement preventing system abuse.
Implemented MCP (Model Context Protocol) prompt engineering with three specialized generators eliminating RAG infrastructure while maintaining response quality.
Designed intelligent model selection algorithm routing Practice Questions to O1, complex DSA to O1, articles to GPT-3.5, and general questions to O3Mini for optimal performance.
Developed advanced response processing pipeline with autoWrapCode (10+ language detection), formatAIResponse (markdown fixing), and removeConversationalEndings (AI fluff removal).
Created scalable session management with three MongoDB schemas (generic, roadmap-specific, content creation), soft deletion, voting system, and optimized query patterns.
Built complete API security layer with JWT authentication, rate limiting, input sanitization, HTTPS enforcement, and comprehensive error handling across 6+ endpoints.
Implemented production monitoring system with response time tracking, token usage analytics, structured logging, and health checks for continuous optimization.
Achieved 3x user engagement and 2x resolution speed through intelligent model selection, clean response formatting, and context-aware interactions.
Engineered no-RAG architecture using sophisticated prompt engineering instead of vector databases, reducing infrastructure costs by 60%.
Added content caching optimization with RoadmapAskAIContentCreation schema and duplicate request prevention for article improvements.
Implemented question classification system using GPT-3.5 Turbo to categorize questions into 7 types (DSA, System Design, Development, etc.) for better routing.
Designed circuit breaker pattern and fallback chains (O1 → O3Mini → GPT-3.5 → Llama) for API failure resilience and graceful degradation.

System Architecture & Details

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Architected and deployed a production-grade AI-powered global search engine across ProPeers platform serving Roadmaps, Digital Products, Mentors, Webinars, and Bootcamps from a single query.
Engineered dual-model AI orchestration using Kimi-K2.5 for intent extraction and GPT-5.2 for response generation via Microsoft Azure Foundry, enabling sub-second intelligent query understanding.
Built a 3-collection ChromaDB vector search system with separate collections for Roadmaps (roadmapsearchdata-v0), Digital Products (dpsearchdata-v0), and Mentors (mentorsearchdata-v0) using Azure OpenAI text-embedding-ada-002.
Implemented hybrid search pipeline combining semantic vector search (ChromaDB) for Roadmaps/DPs with tag-overlap scoring for Mentors, achieving 0.62 score threshold for relevance filtering.
Designed intent extraction engine using Kimi-K2.5 that parses user queries including Hinglish, short-form, and vague inputs into structured JSON with intent, goal, topic_tags, and audience classification.
Built full data ingestion pipeline with MongoDB fetch scripts, Azure OpenAI embedding generation, batch processing (5 docs/batch with rate limiting), and ChromaDB storage across 3 collections covering 100+ roadmaps, 18+ DPs, and 20 top mentors.
Implemented deduplication logic for Digital Products and parallel search across all 3 ChromaDB collections using Promise.all, reducing search latency significantly.
Integrated live MongoDB fallback layer for Webinars and Bootcamps - fetching isActive records directly from DB in parallel with ChromaDB search, enriching LLM context without RAG overhead.
Developed score-based relevance filtering with threshold 0.62 - Cooking/gibberish queries correctly trigger fallback while specific queries like 'Goldman Sachs ECHP' achieve 0.78+ similarity scores.
Created 19 smart suggestion chips with intent-optimized queries covering DSA, System Design, Full Stack, AI/ML, DevOps, Cloud, and Career Switch - sorted by user search frequency for maximum discoverability.
Built complete Next.js frontend with Tailwind CSS featuring search bar, smart chips, result cards for all 5 content types (roadmaps, DPs, mentors, webinars, bootcamps), AI response display, and fallback handling.
Validated system across 6 edge case categories - Easy (React query: 0.69 avg), Medium (Hinglish career switch: 0.74), Hard (out-of-scope cooking: fallback triggered), Gibberish (fallback triggered), Single-word (DSA: 0.74), and Niche (Goldman Sachs: 0.786).
Designed 3-step LLM response generation: Kimi extracts structured intent → ChromaDB+MongoDB fetch results → GPT-5.2 generates 60-word motivational summary with structured result presentation.
Implemented Microsoft Azure Foundry model integration for both GPT-5.2 (max_completion_tokens) and Kimi-K2.5 (system role normalization, think-tag stripping, 3000 token allocation for reasoning).

System Architecture & Details

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Engineered an AI-integrated code editor using Monaco, seamlessly tied into CodeLLM and AskAI pipelines.
Supported live verdicts, multi-language (C++, Java, Python) switching, and dynamic prompts based on user activity.
Embedded AI-based feedback inline within the editor via backend event sync and code stream capture.
Delivered interactive IDE-like experience with <40ms event lag, boosting engagement and retention by 40%.
Tight integration with RoadmapAI and CodeLLM for contextual assistance
Real-time code validation and suggestions during typing

System Architecture & Details

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Refactored and optimized over 150 core APIs (Editor, Roadmap, AskAI, Profile) for high-throughput performance.
Reduced average response latency from 2.2s → 300ms through async queues, parallel batches, and Redis caching.
Introduced pagination layers, ElasticSearch indexing, and horizontal load balancing to maintain SLA under scale.
Achieved 70% backend performance boost and improved Core Web Vitals (TTFB, LCP, FCP) across all pages.
Load tested to 10K RPM 99.95% uptime sustained with zero cold-starts using warmed cloud functions.
Implemented advanced caching strategies and async processing
Enhanced frontend performance through SSR, dynamic imports, and lazy-loading

System Architecture & Details

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Architected an end-to-end Payroll & Payout Automation platform for ProPeers automating the entire monthly compensation lifecycle for mentors and employees - replacing a spreadsheet-driven, manually reconciled process with a fully automated pipeline covering earnings aggregation → line-item breakdown → invoice generation → payslip generation → approval workflow → payment transfer → reconciliation, offloading 80% of manual finance effort every payout cycle.
Engineered a payout cycle engine as a dual-level state machine: each monthly cycle (cron-triggered) transitions through OPEN → LOCKED → PENDING_APPROVAL → PROCESSING → COMPLETED, while every per-mentor payout item inside it carries its own independent status (PENDING → APPROVED → TRANSFERRED / FAILED → RETRY) - so a single failed transfer never blocks the rest of the batch, and partially completed cycles resume exactly where they stopped instead of re-running from scratch.
Built the earnings computation layer on MongoDB aggregation pipelines that join every mentor's sessions, bookings, and mentorship services across the cycle window - computing gross earnings, platform commission, tax deductions (TDS), adjustments, and bonuses per mentor - and persisting the result as immutable line-item earning records so every rupee in a payout is traceable back to the exact session that generated it.
Designed an append-only transaction ledger where every credit, debit, deduction, and transfer is an immutable transaction document guarded by idempotency keys - retries, re-runs, and double-clicks can never produce a double payout - with compound indexes on (mentorId, cycleId, createdAt) powering instant per-mentor statement queries and cycle-level rollups without full collection scans.
Implemented automated invoice and payslip generation per mentor per cycle with unique sequential document numbering, full line-item earnings breakdown, commission and tax fields, and downloadable PDF documents - generated the moment a cycle is approved and emailed automatically, eliminating the manual creation of hundreds of financial documents every month.
Developed a maker-checker approval workflow for the finance/admin team: a review dashboard surfaces the complete computed cycle with per-mentor breakdowns before any money moves, supporting bulk approval, per-item hold, and dispute flagging - with a complete audit trail recording who approved what and when, so every payout decision is accountable and reviewable.
Built the payment transfer layer with failure isolation: approved payouts dispatch in batches with automatic retry and backoff on transient failures, transfer confirmations are reconciled against the ledger to catch mismatches before cycle close, and failed items park in a retry queue with alerting instead of silently disappearing - guaranteeing zero missed and zero duplicate payments.
Shipped a self-serve earnings dashboard for mentors: per-session earnings breakdown, complete payout history, payslip and invoice downloads, and current-cycle accrual preview - killing the "where is my payment?" support loop by making every mentor's compensation fully transparent without contacting the finance team.
Enforced payments-grade security and access control: role-based access (admin / finance / mentor) behind JWT authentication, masked bank account details in every UI surface, encrypted account information at rest, and stage-wise email notifications (cycle locked, approved, paid) via the platform's email infrastructure so mentors and employees always know exactly where their money is in the pipeline.

System Architecture & Details

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Posted by princesinghdev

Featured projects - products I designed, built and shipped

Product

Full architecture, demo video and impact for each.

Products Shipped

175

Technologies

Live Demos

Walkthroughs

Next.js 15 (App Router)React 19TypeScript 5Tailwind CSS 4Framer Motion 12Zustand 5 (authStore, aiModelsStore, chatHistoryStore - persisted)Server-Sent Events (SSE token streaming)React Markdown + remark-gfmreact-syntax-highlighterOpenAI (GPT-5.4 Pro, GPT-5.4 Mini, GPT-5.3 Codex)Anthropic (Claude Opus 4.8, Opus 4.7, Sonnet 4.6)Google AI (Gemini 3.1 Pro, Gemini 3 Flash, Gemma 3 27B)xAI (Grok 4.3, Grok 4.2 Reasoning)DeepSeek (V4 Pro, V4 Flash)Meta (LLaMA 4 Maverick)Mistral (Mistral Large 3)Moonshot (Kimi K2.6, K2.5)Qwen (Qwen3 VL)Nvidia (Nemotron Nano)MiniMax (MiniMax M2)+18

Architected ARX (AI Architect) - a standalone, product-grade conversational AI application at /arx built on Next.js 15 (App Router), React 19, TypeScript 5, and Tailwind CSS 4 - giving direct chat access to 24+ frontier models across 11 providers including OpenAI (GPT-5.4 Pro, GPT-5.4 Mini, GPT-5.3 Codex), Anthropic (Claude Opus 4.8, Opus 4.7, Sonnet 4.6), Google (Gemini 3.1 Pro, Gemini 3 Flash, Gemma 3 27B), xAI (Grok 4.3, Grok 4.2 Reasoning), DeepSeek (V4 Pro, V4 Flash), Meta (LLaMA 4 Maverick), Mistral (Mistral Large 3), Moonshot (Kimi K2.6, K2.5), Qwen (Qwen3 VL), Nvidia (Nemotron Nano), and MiniMax (MiniMax M2) - all served through 30+ streaming endpoints with intelligent model routing and sub-300ms first-token latency.
Engineered the full request pipeline surfaced to users as a 6-stage animated workflow: Your Query → JWT Auth & Rate-Limit → RAG Retrieval (ChromaDB vector search pulling full personal context in <1s) → Model Router (picks the best of 24+ frontier models) → AI Inference → Token Streaming - every stage backed by the real production backend: Passport.js JWT validation, Redis-backed rate limiting with structured 429 rateLimit envelopes (limit, remaining, resetInSeconds) that the frontend converts into live countdown timers without polling, and SSE token streaming rendered incrementally in the chat UI.
Grounded every answer through a 3-Layer Hybrid RAG pipeline over 10M+ ChromaDB embeddings (Azure OpenAI text-embedding-3-large on self-hosted Azure VMs): Layer 1 - 13 regex intent-detection rules for zero-latency section routing; Layer 2 - 12 pre-cached semantic anchor embeddings compared via cosine similarity with no extra API calls; Layer 3 - full-corpus similarity search fallback; combined with entity-diverse re-ranking (guarantees ≥1 document per company/project) and keyword priority boosts (critical=+3, high=+1) - achieving ~99% RAG accuracy and 95% hallucination reduction so ARX answers about real experience, real architecture decisions, and real production numbers instead of generic LLM output.
Built a dual-channel model delivery layer: Channel 1 routes through Azure AI Foundry (Anthropic Foundry SDK) and direct provider APIs (Azure OpenAI, Google GenAI, Mistral, DeepSeek, Grok, Llama, Kimi clients); Channel 2 routes through AWS Bedrock (ConverseStreamCommand) for Claude, Qwen, Gemma, MiniMax, Nemotron, and Kimi - with a MongoDB ActiveAIModels registry so models can be added, reordered, or disabled at runtime with zero redeploys; the frontend model selector hydrates from this registry via a Zustand aiModelsStore, letting users switch between any of the 24+ models mid-session without losing conversation context.
Delivered an adaptive token streaming architecture over Server-Sent Events with dynamic chunk sizing (2–8 characters per write) and ±3ms jitter timing to prevent synchronization artifacts across providers with wildly different native token cadences, sustaining <50ms chunk intervals uniformly across all 24+ models - complemented by a ChromaDB-backed response memory cache that re-streams previously computed answers chunk-by-chunk so cached responses feel identical to live inference, eliminating redundant LLM API calls for repeated questions and improving perceived latency by 60%.
Developed an MCP-compliant prompt engineering layer with dynamic system/user role injection and adaptive tone control (professional, casual, friendly, technical) so the same grounded facts render in the register the user asked for, plus Redis-backed long-term session memory preserving conversational context across messages and reconnects; coupled with a daily cron-driven RAG re-ingestion pipeline that automatically clears and re-embeds resume/project data from MongoDB into ChromaDB across 12 structured sections (salary, experience, projects, education, GitHub, mentorship, DSA, skills, achievements, social media, personal, major contributions) - ARX never answers from stale context with zero manual content operations.
Solved the cold-visitor trust problem by making the product demonstrate itself: a live HeroChat that streams a real grounded answer token-by-token before signup, an animated replay of the actual 6-stage request pipeline with live token counting across 3 real Q&A scenarios, and an 11-provider routing console mirroring real dispatch behavior - the architecture is the demo, with next/dynamic code-splitting keeping the animation-heavy page fast on first load.
Built the model dispatch layer as a statically-typed streamHandlerMap - 27 dedicated streaming handler functions keyed by exact provider model ID: askGPT5_4ProChatStream for gpt-5.4-pro, askClaudeOpus48ChatStream for claude-opus-4-8, askClaudeOpus47ChatStream for the Bedrock-namespaced us.anthropic.claude-opus-4-7, askBedrockQwen3ChatStream for qwen.qwen3-vl-235b-a22b, askKimi25ChatStream for moonshotai.kimi-k2.5, askGrok4_2ReasoningChatStream for grok-4-20-reasoning, and 21 more - each handler pre-bound to its correct transport (Azure OpenAI, Anthropic Foundry, Google GenAI, or AWS Bedrock) behind one uniform signature (message, chatHistory, onChunk, onStart, signal), so adding a new frontier model is a single map entry with zero conditional routing logic anywhere in the UI.
Engineered the askAI request lifecycle as a guarded state machine: 120-character input limit → auth check (unauthenticated users routed to /arx/chat/login) → isProcessingRef re-entrancy lock blocking double-submits → abortRef.current?.abort() cancels any in-flight stream before creating a fresh AbortController → optimistic user-message append → isThinking state held until the transport's onStart callback fires on the first received byte → a fullResponse accumulator mirrors every chunk into live streamingContent state → on completion the streamed buffer commits to the message list after a 40ms settle window that prevents double-render flicker at the stream/committed boundary - with AbortError explicitly distinguished from real failures (DOMException name check) so intentional cancels never surface an error bubble, and a finally block guaranteeing loading/processing flags reset on every exit path.
Implemented two custom streaming transports (streamFetcher for raw token streams, sseStreamFetcher for typed JSON events): fetch POST with credentials:include → res.body.getReader() with incremental TextDecoder decoding (stream: true); the SSE variant maintains a partial-line buffer - splitting on newlines, re-buffering the trailing fragment, and parsing only data: -prefixed lines - so JSON events survive network chunk boundaries that split mid-event; both transports check signal.aborted on every read-loop iteration and call reader.cancel() for leak-free teardown; HTTP 429 responses parse the structured rateLimit payload (success, message, rateLimit.resetInSeconds with a safe fallback shape when the body isn't JSON) and push resetInSeconds into the global navbar store - driving accurate countdown timers across the app with zero polling.
Unified cross-product chat history in a single Zustand chatHistoryStore holding three collections (chats, bestvsbest, voicechat) shared by ARX, Versus, and Voice - hydrated by exactly one getUserChatHistory call per authenticated session (an isFetched flag prevents refetch storms), with every completed answer optimistically prepended via prependChat carrying modelId ({name, model}), ragChunksUsed, fromMemory, and responseTime metadata before any server round-trip - history appears instantly in the sidebar and survives reloads.
Engineered the chat UX mechanics: a stickRef tracks whether the user is parked within 80px of the scroll bottom via a passive scroll listener - auto-follow engages during token streaming only while the user hasn't scrolled up and re-engages on send; in-chat search with match-index navigation, a measured model-chip width injected as textarea padding so the placeholder never overlaps the selected-model chip, React Markdown + remark-gfm with syntax-highlighted code blocks, skeleton hydration (ArxChatSkeleton), isAuthChecked-gated route protection (redirect fires only after the auth check resolves - no login-page flash for valid sessions), a dedicated login page (email+OTP, Google OAuth, GitHub OAuth), and a wrong-domain guard with canonical redirect to www.princesinghai.com/arx - all dark/light themed and code-split via next/dynamic.
Hardened the platform behind a 4-layer security perimeter where every layer catches exactly what the previous one structurally cannot: a CORS allowlist (browser-enforced only) → an Origin guard rejecting any request with a missing or non-allowlisted Origin header - the check that actually stops curl and Postman, since a direct HTTP client ignores CORS entirely → an internal-key guard requiring a shared secret held only by the server-side proxy, making the API unreachable except through the application itself → Passport JWT re-read against the database on every request, so a deactivated account's un-expired token stops working immediately.
Engineered the streaming layer as a two-clock decoupling that normalizes 5 structurally different provider stream shapes into one identical cadence: a provider-paced reader loop only fills a buffer, while an independent UI-paced writer loop drains it on a fixed frame using backlog-adaptive chunk sizing (2/4/6/8 characters at <20/<80/<200/≥200 buffered) - a self-balancing rate matcher that sizes up to drain a fast provider's backlog and sizes down to stretch a slow one smoothly, so every model types at the same rhythm and the delivery channel is invisible to the user.
Solved the three independent buffering layers that silently destroy paced streaming in production: Content-Encoding: identity to opt out of gzip, whose compression window would batch carefully-paced 2–8 character chunks back into bursts; X-Accel-Buffering: no to stop the reverse proxy doing the same thing a layer higher; and socket.setNoDelay(true) to disable Nagle's algorithm so small writes leave the kernel immediately instead of coalescing - without all three, the typewriter effect collapses into stutter no matter how well the writer loop is tuned.
Designed the RAG corpus as deterministically generated semantic chunks across 12 sections from a hand-written template generator rather than a recursive text splitter - because the source is structured data, not prose - and moved the expensive work to index time instead of query time: synonyms and alternate phrasings are embedded directly into chunks as SEARCH KEYWORDS, so retrieval costs zero LLM calls per query where a conventional pipeline spends multiple calls on query rewriting and HyDE; redundant confirmation chunks written as explicit question-and-answer pairs make common recruiter questions embed close to their answer form, and priority-weighted metadata guarantees curated summary chunks survive re-ranking instead of being crowded out by literal keyword matches.
Enforced grounding through a strict prompt contract rather than trusting the model to behave: retrieved context is injected as discretely numbered units so the anti-hallucination rule has something concrete to point at, followed by explicit constraints and a self-check validation list - and critically, any client-supplied system message is stripped server-side, so the system prompt cannot be injected or overridden from the request; when retrieval returns nothing the context block is simply empty, and the contract converts a would-be hallucination into a graceful in-persona admission instead.
Built a diagram delivery pipeline that stores raw architecture-diagram source inside the RAG corpus and hands the model a pre-repaired diagram to place verbatim - instead of asking an LLM to reproduce diagram syntax from memory, which reliably corrupts it - with a custom sanitizer that repairs malformed edge labels, unbalanced delimiters, incomplete subgraph blocks, reserved characters inside node labels and every arrow variant, wired to an instruction builder that switches between generating a diagram from scratch, placing the stored one, or showing it as reference only.
Implemented quota control as a structural cost guarantee: a Redis-backed limiter keyed on a composite user-ID + IP - neither alone is sufficient, since a user ID alone lets one person burn quota across devices while an IP alone punishes everyone behind a corporate NAT - deliberately made global across all models rather than per-model so quota cannot be multiplied by switching models, with a fallback to an in-process store when Redis is unavailable so limits degrade rather than disappear, and a resetInSeconds read from the actual Redis TTL rather than a recomputed estimate - making the client countdown accurate to the second with zero polling.
Made compensation privacy a property of the retrieval layer rather than a prompt instruction: the salary chunk is a single dense, numbers-heavy document, which makes it a strong semantic attractor for a wide range of unrelated career questions - so it is unconditionally excluded from every fallback retrieval path and reachable only when intent detection explicitly resolves a compensation question, guaranteeing that salary can never leak into an answer about architecture, projects or experience by construction instead of by hoping the model stays on topic.
Shipped the operational surface that makes runtime configuration real instead of aspirational: role-guarded admin endpoints providing live vector-store chunk CRUD - page through, read, update or delete an individual embedding chunk - so a single incorrect RAG chunk is hot-patched in place without a full re-ingest; and a model-registry cache refresh that completes the zero-redeploy loop, since flipping a model's active flag alone changes nothing while the registry cache is deliberately stored without expiry - the database flip plus the cache refresh is the actual operation.
Made resilience an explicit design property rather than an accident: chat persistence is deliberately fire-and-forget, so database latency can never block an answer that is already streaming, and every save writes through to the cache, turning a subsequent history load into one cache read instead of three database queries; on failure, cache loss drops the limiter to an in-process store while reads fall through to the database, an embedding failure yields an honest refusal instead of a fabrication, and a client disconnecting mid-stream still completes caching and persistence - so provider spend already incurred is never wasted.
Engineered the zero-latency intent router so that rule ordering is load-bearing rather than incidental: compensation is evaluated first, so "what package did you get at ProPeers?" routes to salary instead of employment; company-specific rules precede the generic experience rule so a named employer narrows to that employer rather than returning every job; named projects precede the generic "tell me about" rule so a product question never degrades into a biography; and the router carries fuzzy matching for commonly misspelled company names plus geographic inference - a question about the "US company" resolves to the correct employer because that fact is encoded into the routing layer - all resolved as pure metadata lookups with no embedding call and no LLM call.
Solved a retrieval-quality failure that pure relevance scoring cannot see: because one employer had roughly four times the indexed content of another, a question like "tell me about your work experience" returned four chunks about a single company and zero about the rest - each individually top-scoring, collectively a bad answer, since the user asked about a career, not a company. The fix reserves a guaranteed floor of result slots per distinct entity before filling the remainder by score, then re-sorts for presentation so the strongest chunk still leads - giving breadth across companies and projects without sacrificing the top result, and turning a relevance ranker into a coverage-aware one.
Absorbed real provider-level incompatibilities behind one uniform internal contract instead of leaking them upward: the same grounding prompt is delivered five structurally different ways - as a true system message, as a top-level system parameter, as an instructions field, as a single flattened prompt string, and - on the Bedrock channel, which accepts only user and assistant roles and rejects anything else - as a prepended user turn with every history role normalized to a permitted value; paired with a per-tier inference policy that assigns lower temperature to the models used for the most factual answers and looser sampling to the rest, so adding a provider never changes a single line of dispatch logic.

Key Concepts

Product-Grade AI Chat App - 24+ Frontier Models, 11 Providers, 30+ Streaming Endpoints6-Stage Pipeline: Query → Auth/Rate-Limit → RAG Retrieval → Model Router → Inference → Token Streaming3-Layer Hybrid RAG (13 Regex Intent Rules → 12 Semantic Anchors → Full-Corpus Search)Entity-Diverse Re-Ranking + Keyword Priority Boosts - ~99% RAG AccuracyDual-Channel Delivery: Azure AI Foundry / Direct APIs + AWS BedrockMongoDB ActiveAIModels Registry - Zero-Redeploy Model ManagementMid-Session Model Switching Without Context LossStructured 429 rateLimit Envelopes → Live Countdown UI Without PollingLive Self-Playing Hero Chat Demo (Token-by-Token Streaming Simulation)Interactive Workflow Simulator - 3 Rotating Scenarios, Live Token CounterLive Model-Routing Console - 11-Provider Rail + Terminal Traffic LogIntersectionObserver-Driven Count-Up Stats (Re-Stream Every 5s In View)Persisted Chat History via Zustand + localStorageWrong-Domain Guard with Canonical Redirect (Multi-Deployment Safety)4-Layer Security Perimeter: CORS → Origin Guard → Internal Key Guard → JWTOrigin-Header Enforcement - Blocks curl/Postman Where CORS Structurally CannotAuth + Quota Resolve Before Any Provider Call - Zero Unauthorized LLM SpendTwo-Clock Stream Decoupling: Provider-Paced Reader + UI-Paced WriterBacklog-Adaptive Chunk Sizing as a Self-Balancing Rate MatcherTriple Buffering Defeat: gzip Opt-Out + X-Accel-Buffering + Nagle DisabledIndex-Time Query Expansion - Zero LLM Calls Per RetrievalServer-Side System-Prompt Stripping - Prompt Injection ResistanceCompensation Privacy Enforced at the Retrieval Layer, Not by PromptLive Vector-Store Chunk CRUD - Hot-Patch RAG Without Re-IngestFire-and-Forget Persistence + Write-Through Cache (1 Read, Not 3 Queries)Rule Ordering as Load-Bearing Design in the Intent RouterCoverage-Aware Re-Ranking - Guaranteed Entity Floor Before Score FillOne Grounding Prompt, Five Provider-Specific Delivery ShapesPer-Tier Inference Policy - Lower Temperature for Factual Answers

Impact

Served as the flagship chat surface of a platform tested with 1000+ active users, processing 18,000+ AI queries from 12,000+ unique visitors and gathering 800+ detailed user feedback that drove 3 major product iterations to a 99% user satisfaction rate - all with no API keys required from users.
Achieved ~99% RAG accuracy and 95% hallucination reduction through the 3-Layer Hybrid RAG pipeline over 10M+ ChromaDB embeddings - Layers 1 and 2 (13 regex intent rules + 12 pre-cached semantic anchors) resolve most queries with zero additional embedding API calls, and full context retrieval completes in <1 second, so every answer is grounded in verifiable production facts instead of generic LLM filler.
Sustained sub-300ms first-token latency across all 24+ models and 30+ streaming endpoints via dual-channel delivery - when a provider degrades, traffic reroutes between Azure AI Foundry and AWS Bedrock with zero downtime, so a single provider outage never takes ARX offline.
Improved perceived response latency by 60% through adaptive SSE streaming (2–8 char chunks, ±3ms jitter, <50ms intervals) combined with the ChromaDB response memory cache - repeated questions replay instantly from cache while feeling identical to live inference, cutting redundant LLM token spend with zero buffering under high load.
Eliminated model-release lag entirely: the MongoDB ActiveAIModels registry means new frontier models (GPT-5.4, Claude Opus 4.8, Grok 4.3) go live to users within minutes of provider release by flipping a database record - zero code changes, zero redeploys - keeping ARX permanently current in a market where model generations turn over monthly.
Reduced API abuse by 99% on infrastructure handling 50K+ daily API calls: every chat request clears JWT auth and Redis-backed rate limiting before any model call is made, so not a single unauthorized request has ever triggered LLM spend - with structured 429 envelopes rendering accurate live countdown timers without any frontend polling.
Kept answers permanently fresh with zero manual content operations: the daily cron re-ingestion pipeline re-embeds all 12 data sections from MongoDB into ChromaDB automatically, so profile, project, and experience updates propagate into RAG answers within 24 hours without anyone touching the pipeline.
Built on the same production AI infrastructure powering systems that reached 600K+ users, drive 80%+ of platform traffic, and run a <40ms AI inference review engine - ARX is a customer-facing window into battle-tested architecture, not a demo assembled for screenshots.
Generated direct inbound opportunity: multiple job calls and offers from AI companies, and 3–4 founders reached out to build similar multi-model chat products after using ARX and seeing the architecture respond in real time - the product itself became the strongest proof of engineering capability.
Made unauthorized model spend structurally impossible rather than merely unlikely: because JWT verification and quota checks both resolve before the controller is entered, there is no code path in which an unauthenticated or over-quota request reaches a provider API - and the 4-layer perimeter rejects a direct curl for a missing Origin header before authentication is even attempted, closing the exact gap that CORS alone structurally cannot cover.
Cut retrieval cost to zero LLM calls per query by moving query expansion into the corpus at index time instead of paying for query rewriting on every request - where a conventional RAG pipeline spends multiple LLM calls per query on rewriting and HyDE, this one spends none, and intent routing resolves the most common questions with no embedding call at all, turning them into pure metadata lookups while the remaining layers absorb everything else.

System Architecture & Details

Select a view to see the architecture flow

GitHub

Posted by princesinghdev

5000+ Problems Solved Across 10+ Platforms

Milestone

Tracked across 6 competitive platforms.

1400+

LeetCode

1300+

GeeksForGeeks

560+

InterviewBit

2000+

CodeStudio

300+

HackerRank

Problems solved per platform

HackerEarthHackerRankInterviewBitGFGLeetCodeCodeStudio

Key Highlights

5000+ Problems Solved Across 10+ Platforms
1700+ Day Unbreakable Coding Streak
Knight Badge @LeetCode (Top 5% Worldwide)
InterviewBit Global Rank 13 (6⭐ Problem Solving)
Institute Rank 1 & Global Rank 98 @GeeksForGeeks

LeetCode

1879+ (Top 5% Worldwide)

1400+ solved

4⭐ Problem Solving

GeeksForGeeks

Institute Rank 1 & Global Rank 98

1300+ Solved solved

6⭐ Problem Solving

InterviewBit

1854+ (Master)

560+ Solved solved

Rank: Global Rank 13

CodeStudio

1854+ (Specialist)

2000+ solved

Rank: Global Rank 130

6⭐ Problem Solving

HackerRank

6⭐ Problem Solving

300+ solved

Rank: Rank 52

HackerEarth

1260+ Top 10%

200+ solved

Rank: Rank 101

5⭐ Python/Java

LeetCode

Posted by princesinghdev

Trophy case - 11 recognitions earned along the way

Recognition

Rankings, badges and milestones from the last few years.

Headline Wins

Recognitions

4.8/5

Mentor Rating

Knight1879+ (Top 5% Worldwide)

1400+LeetCode

1300+Institute Rank 1

560+Global Rank 13

2000+CodeStudio

300+HackerRank

Cracked 4 Remote Jobs as Fresher

Top 5% Worldwide on LeetCode

Global Rank 13 on InterviewBit

Mentored 40K+ Students

600K+ Platform Users for Built Features

Knight Badge (LeetCode)

6⭐ Problem Solving (InterviewBit)

Institute Rank 1 (GFG)

Annual Awards 2022 and 2023 on LeetCode

Include Top 0.4% of the LeetCoders

Include in Top 5% Code in the World

Problem-solving highlights

5000+ Problems Solved Across 10+ Platforms
1700+ Day Unbreakable Coding Streak
Knight Badge @LeetCode (Top 5% Worldwide)
InterviewBit Global Rank 13 (6⭐ Problem Solving)
Institute Rank 1 & Global Rank 98 @GeeksForGeeks

Posted by princesinghdev

The stack I build on - 118 technologies across 8 domains

Stack

LLMs, AI/ML, backend, cloud, data and tooling.

118

Technologies

LLMs

AI / ML

Cloud & DevOps

HOT SKILLS

AI / ML

RAGLLMsLLMs OrchestrationMulti LLMs OrchestrationxAIMCP OrchestrationLLMOps / AIOpsPyTorchLangChainscikit-learnToken StreamingLLM Fine-TuningCaching & MemoizationPrompt EngineeringTool-Calling AgentsAI Memory SystemsInference OptimizationMulti-Model RoutingLow-Latency PipelinesRetrieval StrategiesMultimodal AI

Frontend Development

Next.jsReactReduxCSSHTMLSSRCSRReact QueryTailwindCSSHybrid RenderingBootstrap

Backend Development

GolangNode.jsFastAPIExpressDjango.NET

Cloud & DevOps

DockerAWSAzureTerraformCI/CDKubernetesGitHub ActionsGitLab ActionsJenkinsGrafanaPrometheus

Databases

RedisMongoDBMySQLChromaDBFirebaseVector DBsCache DesignTTL CachingVector SearchHybrid Search

Programming Languages

PythonTypeScriptJavaScriptSQLJavaC++Bash

Tools

GitFigmaLinuxGitHubScrapyVS CodePyCharmPostmanSeleniumIntelliJ IDEAGitHub Copilot

Posted by princesinghdev

GitHub analysis - contributions, streaks and language mix

Activity

3000+ contributions since 2020.

3000+

Contributions

540

Commits

270

Pull Requests

318

Stars Earned

Language mix across repositories

Next.js100%
JavaScript90%
TypeScript90%
Express.js90%
Python90%
Node.js80%
HTML80%
React50%
CSS40%
Bash / Shell40%
C++30%
Java20%

Loading Contributions Status...

Profile

Posted by princesinghdev

B.Tech in Computer Science - Sage University Indore

Education

2020 – 2024 · MP, India

8.5/10

CGPA

2020 – 2024

Duration

MP, India

Campus

Sage University Indore

B.Tech in Computer Science

2020 – 2024 · MP, India

CGPA: 8.5/10

About Me

I'm a Founding Engineer & AI Architect with 2.5 years of hands-on experience building large-scale AI systems, distributed backend infrastructures, and production-grade full-stack platforms. At ProPeers, I own and engineer the core systems that power 80%+ of total platform traffic including Roadmaps, RoadmapAI, AskAI, CodeLLM, Global AI Search, and the Contextual AI Code Editor. I design high-scale backend architectures, real-time data pipelines, aggregation engines for 600K+ users, Redis-backed caching layers, search-validation systems, role-based access flows, rate-limiting frameworks, and CI/CD deployment automation that cut release time by 34% and improved reliability across 150+ microservices. Through SSR, dynamic imports and hybrid rendering patterns, I’ve reduced key user journey response times from 1.1s → 200ms, delivering a noticeably smoother product experience. As an AI Architect, I build Agentic AI pipelines, RAG retrieval systems, MCP protocol layers, and multi-model inference workflows using Azure OpenAI, Azure Databricks, GPT models, and Llama 3.x OSS models. My work spans token optimization, context-window compression, semantic chunking, and adaptive prompt engineering to deliver intelligent experiences at under real production traffic. I’ve engineered RoadmapAI with a self-learning RAG pipeline (text-embedding-ada-002, ChromaDB, semantic filters, vector enrichment), achieving ~99% roadmap accuracy and lifting roadmap ratings from the early 12% baseline. I built CodeLLM, a production AI judge featuring multi-language detection, dual-layer JSON parsing, COMPILATION/RUNTIME/VALIDATION error classification, and deterministic verdict synthesis for educational code evaluation. I developed AskAI with MCP-layered prompts, resource-type detection (roadmap/article/practice), O1/O3 model routing, token metering, and auto-structured responses improving resolution speed 2× and engagement 3×. I also built the AI Code Editor with ~40ms inference, inline reasoning, multi-language execution, and deep integration with RoadmapAI and CodeLLM, significantly boosting editor retention. Beyond AI flows, I’ve implemented token-based tiered access systems (one-time/monthly/yearly) on top of these capabilities, and engineered self-optimizing RAG pipelines and distributed multi-model inference workflows that balance accuracy, cost and latency under real-world traffic. On the product and platform side, I’ve delivered Individual Roadmap Communities, scalable live-stream pipelines, error-resilient API layers, multi-step onboarding flows, connected roadmap progress engines, and search validation systems ensuring hallucination-free retrieval across Roadmaps and RoadmapAI. At the infrastructure layer, I’ve reduced downtime by 90% (4 hours → 45 mins/month), stabilized Azure VM workloads, eliminated Bastion and high-cost D8 VM footprints, fixed bandwidth cost spikes, and built high-availability fallback layers with cache-first routing and distributed failover. Day-to-day, I work across MERN + TypeScript, Node.js microservices, Docker/Kubernetes, Azure Cloud, Databricks, CI/CD automation, Prometheus/Grafana observability, and async caching pipelines powering 100K+ monthly active operations. Outside core engineering, I’m a Problem-Solving & DSA Enthusiast with 5000+ problems solved, a 1700+ day coding streak, and top 0.1% global rankings across platforms. As a mentor to 40,000+ learners, I help engineers master DSA, System Design, Development, DevOps, and Remote Job Preparation, guiding them from theory to real-world success. I love building scalable systems, intelligent architectures, and next-generation AI-first engineering experiences that blend reliability, performance, and deep technical innovation.

Founding Engineer & AI Architect @ ProPeers
SDE - 1 @ ProPeers
Junior Software Engineer @ Cloud Conduction

Say Hello

Status

Community Reach

100,000+7 channels

Engineers Mentored

40,000+4.8/5 rating

Systems Shipped

1414 live

DSA Problems

5000+10+ platforms

Coding Streak 1700+ days

Trophy Case (11)

Knight1879+ (Top 5% Worldwide)

1400+LeetCode

1300+Institute Rank 1

560+Global Rank 13

2000+CodeStudio

300+HackerRank

Cracked 4 Remote Jobs as Fresher
Top 5% Worldwide on LeetCode
Global Rank 13 on InterviewBit
Mentored 40K+ Students
600K+ Platform Users for Built Features
Knight Badge (LeetCode)

+5 more recognitions

Live System Status

All Systems Operational

Systems Live

14in production

LLMs Wired

28frontier models

Coding Streak

1700+ daysunbroken

Contributions

3000+since 2020

6,800+ Visitors

Top Projects

AI Systems I Own

DSA Highlights

5000+ Problems Solved Across 10+ Platforms
1700+ Day Unbreakable Coding Streak
Knight Badge @LeetCode (Top 5% Worldwide)
InterviewBit Global Rank 13 (6⭐ Problem Solving)
Institute Rank 1 & Global Rank 98 @GeeksForGeeks

Tech Stack

LLMs · 28

GPT o1GPT 5.5GPT 5.4GPT 5.2GPT 5.1GPT o3-mini+22

AI & Ml · 27

LLMs OrchestrationMulti LLMs OrchestrationRAGLLMsxAIMCP Orchestration+21

Frontend · 11

Next.jsReactReduxCSSHTMLSSR+5

Backend · 6

GolangNode.jsFastAPIExpressDjango.NET

Cloud & Devops · 11

DockerAWSAzureTerraformCI/CDKubernetes+5

Databases · 17

RedisMongoDBMySQLChromaDBFirebaseVector DBs+11

Languages · 7

PythonTypeScriptJavaScriptSQLJavaC+++1

Tools · 11

GitFigmaLinuxGitHubScrapyVS Code+5

GitHub Snapshot

Stars earned

318

Followers

1160

Public repos

330

Profile grade

A+

Mentorship

40,000+

Engineers mentored

4.8/5

Mentor rating

Data Structures & AlgorithmsFull Stack DevelopmentInterview PreparationPlacement GuidanceAI-Driven Career GuidanceResume Review+5

Book a 1:1 session

Community Impact

Education

Sage University Indore

B.Tech in Computer Science

2020 – 2024 · MP, India

CGPA 8.5/10

AI Engineer Portfolio - Prince Singh | LLM, RAG, Full Stack, Cloud, DevOps

AI/ML Engineer | LLM Engineer | RAG Developer | Full-Stack Engineer | Founding Engineer | Cloud & DevOps Engineer

Large Language Model (LLM) Engineering

RAG Pipeline Engineering

Agentic AI & Multi-Agent Systems

AI Product Engineering

Full-Stack Engineering (React, Next.js, Node.js, TypeScript)

Cloud Engineering & DevOps

System Design & Architecture

Competitive Programming & DSA

Founding Engineer Experience

Remote AI Engineer | Global Collaboration

Software Engineer Portfolio

Developer Portfolio

Prince Singh

Building production systems, tuned to the last millisecond & dollar.

From intern to Founding Engineer - 3 roles across 2 companies

Impactful work as an individual contributor

Cloud Cost Optimization & Infra/DevOps Automation (₹222K → ₹185K/month)

System Architecture & Details

RoadmapAI - Multi LLM & RAG Orchestrator

System Architecture & Details

CodeLLM Multi-Model Code Judge

System Architecture & Details

AskAI (AI Peer Programming Assistant)

System Architecture & Details

Global AI Search (Intent-Based Intelligent Discovery Engine)

System Architecture & Details

AI-Powered Code Editor ( Code with AI Agent )

System Architecture & Details

API Optimization ( Scaling 150+ Microservices )

System Architecture & Details

Payroll - Automated Mentor Payout & Payment Infrastructure

System Architecture & Details

Featured projects - products I designed, built and shipped

ARX - AI Architect You Can Talk To

Key Concepts

Impact

System Architecture & Details

Versus - Best vs Best AI Model Arena

Voice with 24+ Brains - Real-Time Voice-to-Voice AI

AskDSA - Your AI Peer Programmer

Multi Model AI Orchestration & Comparison Platform

Multi Cloud Full-Stack AI Platform

5000+ Problems Solved Across 10+ Platforms

Key Highlights

LeetCode

GeeksForGeeks

InterviewBit

CodeStudio

HackerRank

HackerEarth

Trophy case - 11 recognitions earned along the way

The stack I build on - 118 technologies across 8 domains

AI / ML

Frontend Development

Backend Development

Cloud & DevOps

Databases

Programming Languages

Tools

GitHub analysis - contributions, streaks and language mix

B.Tech in Computer Science - Sage University Indore

Sage University Indore

Prince Singh

Hello! 👋