Founding Engineer & AI Architect @ProPeers | Ex-SDE @CloudConduction | Mentor @ProPeers & @Topmate.io | Building @PreparationStreet | Architecting Agentic AI · RAG · MCP · Multi-Model LLM Orchestration · AWS · Azure · AIOps · DevOps | LangChain · ChromaDB | 600K+ Users | Mentored 40K+ Students | 67K+ @LinkedIn | LeetCode Knight 👑 3.5⭐ Top 5% Max(1876) | 1500+ DaysOfCode @LeetCode | GFG Institute Rank 1 | InterviewBit Global Rank 13 | CodeStudio Specialist Max(1854) | 6⭐ Problem Solving | Amateur @HackerEarth | 5000+ Problems Solved | DSA & DEV Mentor | HardCore DSA Lover ❤️ | 75DaysHardPlacementChallenge | System Design · Full-Stack · Open Source

• 🏗️ Founding Engineer & AI Architect - Why I Fit in Your Bucket
• 🚀 Architecting Agentic AI · RAG Pipelines · MCP · Multi-Model LLM Orchestration at scale for 600K+ Users across AWS & Azure
• 🧠 Hardcore DSA Enthusiast & Problem-Solving Addict - 5000+ problems solved with 1500+ Days of Consistent Code across all major platforms, Passionate About Crafting Efficient Code
• 👨‍💻 Tech Explorer - Love to learn new technologies and explore new sets of areas
• ✋ Hand-Holding Expertise: MERN · DevOps · AIOps · Networking · Servers · System Design · Cost Optimization · AI Architectures (RAG + MCP + LLM Ops + Agentic AI)
• 🎓 Top-Rated Mentor - Mentored 40,000+ students and professionals over the last 1.5 years on DSA, Development, Career Growth, Remote Job Prep & Interview Preparation
• 👁‍🗨 Open Source Contributor
• 👨‍🏫 Mentor on @Topmate and @ProPeers
• 📚 Building Multi Model AI Orchestration Platform, PrinceSinghAI, PrinceSinghDev, Preparation Street

• 📞 Book Your 1:1 Guidance DSA & Development & Placement & Career Guidance - Topmate and ProPeers
• ⚡ For Fun Games, Roasting, Memes, HipHop

• 🌟 Led the launch of Roadmap AI, a fully personalized learning assistant powered by RAG (Retrieval-Augmented Generation), OpenAI’s text-embedding-ada-002, Chroma Vector DB, and Modal for real-time, scalable inference — achieving <1s latency and serving 10K+ daily queries.

  • Architected a self-learning adaptive RAG pipeline:
    [JSON → Embedding → Chroma DB → Query Context Retrieval → Prompt Masking → Model → Nested JSON Output]

    • Built zero-context fallback logic, dynamically deciding when to retrieve context or generate from scratch for complete personalization.
    • Injected prompt templates based on match confidence, with automatic re-embedding of new data — making the system truly self-evolving.

• 🧠 Integrated and deployed the Model Context Protocol (MCP) — a dynamic orchestration engine for multi-model routing, confidence-based context injection, and structured prompt masking across models (gpt-3.5, o3-mini, o1).

• 🧩 Extended the Modular Content Pipeline (MCP) to process and vectorize 100+ Roadmaps, powering semantic retrieval and structured AI roadmap generation.

• ⚙️ Built an AI-powered DSA Code Editor supporting Run/Submit/Save with contextual LLM assistance, integrated across gpt-3.5, o3-mini, and o1 — delivering real-time code insights and learning support.

• 🧮 Designed token-based access control with tiered latency modes, real-time usage metering, and premium upsell logic — optimizing engagement and monetization.

• ⚡ Achieved sub-second inference performance across all tiers with caching, async processing, and parallel embeddings, boosting user retention and responsiveness.

• 🔍 Enhanced AskAI with contextual node + discussion integration, improving query relevance and contextual assistance accuracy by 40%.

• 📈 Drove 3x increase in roadmap completions, reduced churn, and transitioned the platform into a self-adaptive AI-first learning ecosystem with real-time LLM intelligence.

July 2024 – July 2025
SDE - 1 · Delhi, India · Remote

• 🚀 Built and scaled the flagship "Roadmaps" feature, delivering 100+ curated learning paths across DSA, Development, and System Design — used by 100K+ 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.

• 🤖 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.

• Worked on complex APIs to reduce processing time and improved tab switching experience for smoother navigation

• 📹 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.

• 💡 Worked with cross-functional teams to integrate AI personalization, improving engagement metrics and completion rates by 40%.

Jan 2024 – June 2024
Junior Software Engineer · Oak Park, Michigan, United States · 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.

🚀 Excited to Share My Coding Journey and Accomplishments! 🚀

🚀 I'm thrilled to showcase my dedication and passion for problem-solving in the world of coding, over the past few years, I’ve solved 5000+ DSA problems across 10+ top coding platforms with an unbreakable 1400+ day streak of daily practice. This journey reflects my relentless focus on logic, consistency, and mastery of core CS fundamentals.

LeetCode and GeeksForGeeks 🏆

• Profile 1: 1300+ problems solved, 3.5⭐ with a max rating of 1660 and 50+ Badges 🥇.
• Annual Awards 2022 and 2023 on LeetCode and Include Top 0.4% of the LeetCoders 🌐.
• Profile 2: 800+ problems solved, 2⭐ with a max rating of 1876 and 10+Badges 🥇 with Knight 👑 Tag and Include in Top 5% Code in the World 🌎.
• Annual Award 2023 and Include Top 4.2% of the LeetCoders 🌐.
• GeeksForGeeks: 1300+ problems solved, Global Rank 100 and Monthly Rank 99 with the Score of 4000+, and Instutie Rank 1🔥.

CodeStudio & InterviewBit & HackerRank & HackerEarth 🏆

• CodeStudio: 2000+ problems solved with 100000+ Coding Score, including in the top 0.5% 🌟.
• InterviewBit: 560+ problems solved, Global Rank 13, and a 6⭐ in Problem Solving 🌐.
• HackerRank: 300+ problems solved, with a coding score of 119000+ 🚀.
• HackerEarth: 5⭐ in Python, 5⭐ in Java, and 5⭐ in Days of Code 🌈.

work@Tech 🏆

• work@Tech: 1510 score, 999 rank, and 40 problems solved, with the best global rank under 1K 🚀.

I'm proud of my continuous growth and learning in the coding world. I am looking forward to more challenges and achievements ahead! 💻🚀

Python	JavaScript	TypeScript	LangChain	OpenAI API	RAG	PyTorch	ChromaDB	Vector DB
Prompt Eng.	React	Next.js	Node.js	Express.js	FastAPI	Django	.NET	Prisma
Redux	React Query	Tailwind CSS	Bootstrap	HTML	CSS	SSR	Hybrid Rendering	Linux
Bash	C++	Java	MongoDB	MySQL	PostgreSQL	Redis	Firebase	ChromaDB
Vector Search	AIOps	System Design	API Optimization	Docker	Kubernetes	AWS	Azure	Jenkins
GitHub Actions	GitLab Actions	Terraform	Grafana	Prometheus	Git	GitHub	GitHub Copilot	VS Code
PyCharm	IntelliJ IDEA	Figma	Postman	Selenium	Scrapy

🔗 Live: ai.princesinghai.com

• Architected a production-grade AI multi-model orchestration platform with three distinct phases: Phase 1 (AI Chat) integrating 20+ latest models including OpenAI (GPT-5.2, GPT-5.1), Google (Gemini 3, Gemini 3 Pro, Gemini 3.1 Pro, Gemma 3), Anthropic (Claude Opus 4.6, Opus 4.5, Opus 4.1, Sonnet 4.5, Sonnet 4), xAI (Grok 4), Meta (LLaMA 4 Maverick), Mistral (Mistral 3), DeepSeek (DeepSeek 3.2), Qwen (Qwen3), MiniMax (MiniMax M2), Nvidia (Nemotron Nano), and Moonshot (Kimi K2.5, Kimi K2.2), with intelligent model routing, token streaming, and context window optimization achieving sub-300ms first-token latency.

• Engineered Phase 2 (Best vs Best Comparison Mode) enabling parallel execution of 2–4 models simultaneously (with capability to handle up to 8) for the same query, supporting GPT-5.2, Gemini 3 Pro, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4, LLaMA 4 Maverick, Mistral 3, DeepSeek 3.2, and Kimi K2.5 with side-by-side response rendering, latency benchmarking, and quality scoring – allowing users to visually compare outputs and select the best result, processing 10M+ tokens distributed during testing with efficient resource utilization across parallel executions.

• Built Phase 3 (Voice-to-Voice Mode) supporting Claude Opus 4.6 and Kimi K2.2 with real-time speech recognition (Azure Speech), voice activity detection, and streaming text-to-speech with natural prosody, achieving <300ms end-to-end voice latency and enabling conversational AI for visually impaired users and hands-free interaction with 7+ language support.

• Implemented a unified RAG pipeline with ChromaDB on Azure VMs (Central India) storing 10M+ embeddings, providing semantic context retrieval with 0.25 similarity threshold and topic-aware filtering to deliver hallucination-resistant responses across all three phases, achieving 95% reduction in hallucinations.

• Designed an MCP-compliant prompt engineering layer with dynamic system/user role injection, adaptive tone control (professional, casual, friendly, technical), and long-term memory using Redis for session persistence, enabling context-aware conversations that remember user preferences and conversation history.

• Created a fine-tuning orchestration engine that allows per-model prompt customization and response formatting (JSON, markdown, plain text), ensuring consistent output structure across different models and enabling seamless switching between phases with zero configuration changes.

• Developed a comprehensive security layer with JWT authentication, Google/GitHub OAuth, rate limiting (3 requests/minute per user), API key validation, and IP-based blocking to prevent unauthorized access and abuse, processing 50K+ daily API calls with zero security breaches and 99% reduction in API abuse.

• Built a real-time token streaming architecture using Server-Sent Events (SSE) and WebSockets, delivering incremental responses with <50ms chunk intervals, and implemented context streaming for long conversations, reducing perceived latency by 60% and improving user engagement.

• Integrated Azure Communication Services for real-time chat and video consultations between users and AI mentors, supporting 1000+ concurrent sessions with <50ms latency, and added Azure Speech Services for voice-to-voice interaction with 7+ language support including English, Hindi, Spanish, French, German, Japanese, and Mandarin.

• Optimized multi-cloud infrastructure using AWS CloudFront CDN for static assets, Azure Load Balancers for compute, and strategic Redis caching, achieving sub-100ms API responses for 90% of requests and reducing infrastructure costs by 25% through intelligent auto-scaling and dynamic model routing based on cost/latency optimization.

🔗 Live: www.princesinghai.com

• Architected a production-grade AI multi-model orchestration platform with three distinct phases: Phase 1 (AI Chat) integrating 20+ latest models including OpenAI (GPT-5.2, GPT-5.1), Google (Gemini 3, Gemini 3 Pro, Gemini 3.1 Pro, Gemma 3), Anthropic (Claude Opus 4.6, Opus 4.5, Opus 4.1, Sonnet 4.5, Sonnet 4), xAI (Grok 4), Meta (LLaMA 4 Maverick), Mistral (Mistral 3), DeepSeek (DeepSeek 3.2), Qwen (Qwen3), MiniMax (MiniMax M2), Nvidia (Nemotron Nano), and Moonshot (Kimi K2.5, Kimi K2.2), with intelligent model routing, token streaming, and context window optimization achieving sub-300ms first-token latency.

• Engineered Phase 2 (Best vs Best Comparison Mode) enabling parallel execution of 2–4 models simultaneously (with capability to handle up to 8) for the same query, supporting GPT-5.2, Gemini 3 Pro, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4, LLaMA 4 Maverick, Mistral 3, DeepSeek 3.2, and Kimi K2.5 with side-by-side response rendering, latency benchmarking, and quality scoring – allowing users to visually compare outputs and select the best result, processing 10M+ tokens distributed during testing with efficient resource utilization across parallel executions.

• Built Phase 3 (Voice-to-Voice Mode) supporting Claude Opus 4.6 and Kimi K2.2 with real-time speech recognition (Azure Speech), voice activity detection, and streaming text-to-speech with natural prosody, achieving <300ms end-to-end voice latency and enabling conversational AI for visually impaired users and hands-free interaction with 7+ language support.

• Implemented a unified RAG pipeline with ChromaDB on Azure VMs (Central India) storing 10M+ embeddings, providing semantic context retrieval with 0.25 similarity threshold and topic-aware filtering to deliver hallucination-resistant responses across all three phases, achieving 95% reduction in hallucinations.

• Designed an MCP-compliant prompt engineering layer with dynamic system/user role injection, adaptive tone control (professional, casual, friendly, technical), and long-term memory using Redis for session persistence, enabling context-aware conversations that remember user preferences and conversation history.

• Created a fine-tuning orchestration engine that allows per-model prompt customization and response formatting (JSON, markdown, plain text), ensuring consistent output structure across different models and enabling seamless switching between phases with zero configuration changes.

• Developed a comprehensive security layer with JWT authentication, Google/GitHub OAuth, rate limiting (3 requests/minute per user), API key validation, and IP-based blocking to prevent unauthorized access and abuse, processing 50K+ daily API calls with zero security breaches and 99% reduction in API abuse.

• Built a real-time token streaming architecture using Server-Sent Events (SSE) and WebSockets, delivering incremental responses with <50ms chunk intervals, and implemented context streaming for long conversations, reducing perceived latency by 60% and improving user engagement.

• Integrated Azure Communication Services for real-time chat and video consultations between users and AI mentors, supporting 1000+ concurrent sessions with <50ms latency, and added Azure Speech Services for voice-to-voice interaction with 7+ language support including English, Hindi, Spanish, French, German, Japanese, and Mandarin.

• Optimized multi-cloud infrastructure using AWS CloudFront CDN for static assets, Azure Load Balancers for compute, and strategic Redis caching, achieving sub-100ms API responses for 90% of requests and reducing infrastructure costs by 25% through intelligent auto-scaling and dynamic model routing based on cost/latency optimization.

🔗 Live: www.princesinghdev.com

• Architected a production-grade multi-cloud full-stack platform serving princesinghdev.com & ai.princesinghdev.com with 12,000+ unique visitors, processing 18,000+ AI queries and gathering 500+ user reviews, leveraging AWS (primary) with EC2, S3, CloudFront, DynamoDB, SES, Bedrock and Azure (secondary) with Virtual Machines, Communication Services, AI Foundry, achieving 99.99% uptime with sub-100ms automatic failover.

• Engineered a scalable MERN stack backend with Node.js/Express.js handling 10,000+ concurrent connections, implementing connection pooling, request throttling, and response caching to maintain sub-200ms API latency under peak loads.

• Built a Next.js/React frontend with server-side rendering (SSR), dynamic imports, and route-based code splitting, achieving 98+ Lighthouse scores for Performance, SEO, and Accessibility across all pages.

• Designed a hybrid database architecture combining MongoDB Atlas for user profiles and session data with AWS DynamoDB for high-throughput token management and Redis caching layer reducing database load by 65% and achieving sub-5ms cache hits.

• Implemented comprehensive user analytics pipeline tracking user behavior, feature usage, and conversion funnels through custom event tracking, ELK Stack aggregation, and Grafana dashboards, enabling data-driven product decisions that increased user retention by 40%.

• Created a multi-provider authentication system with JWT, Google OAuth, GitHub OAuth and AWS Cognito integration, supporting social logins, email/password, and magic link authentication, achieving zero authentication failures with 99.99% login success rate.

• Developed an AI-powered feedback aggregation engine that processes 500+ user reviews using sentiment analysis and topic modeling to automatically categorize feature requests and bug reports, reducing manual triage time by 80%.

• Built real-time notification system using Azure Communication Services and WebSocket connections, delivering live updates to users about their queries, platform announcements, and personalized recommendations with <50ms latency.

• Implemented automated A/B testing framework with feature flags and gradual rollouts, enabling zero-downtime experimentation on new features and achieving 25% improvement in user engagement metrics.

• Designed comprehensive error tracking and recovery system with Sentry integration, automatic error grouping, and intelligent alerting that reduced mean time to resolution (MTTR) from hours to under 15 minutes.