About
I'm a Founding Engineer & AI Architect with 2 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, and the Contextual AI Code Editor.
I design high-scale backend architectures, real-time data pipelines, aggregation engines for 100K+ 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 1400+ 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.
Experience
ProPeers
Founding Engineer
July 2025 – Present · Delhi, India · Remote
- Architected the full AI ecosystem powering RoadmapAI, CodeLLM, AskAI 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 100K+ 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.
SDE - 1
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 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.
- 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
Junior Software Engineer
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.
Impactful Work As a ( INDIVIDUAL CONTRIBUTOR )
INDIVIDUAL CONTRIBUTOR
I’ve engineered the core of our AI ecosystem RoadmapAI, CodeLLM, AskAI, and the AI Code Editorbuilding Agentic AI pipelines, RAG-driven personalization, MCP-layered orchestration, and multi-model LLM systems that deliver real-time learning guidance, deterministic code evaluation, and context-aware programming assistance at scale. My work spans LLM System Design, Tokenization & Reasoning Flows, Azure OpenAI backed inference, Azure Databricks aligned data pipelines, vectorized context retrieval, and high-availability AI microservices powering the majority of our platform’s intelligence layer. I’ve also strengthened the platform’s foundation by optimizing over 150+ critical APIs for latency, reliability, throughput, and large-scale fault-tolerance.
- Architected an end-to-end RAG-powered AI learning platform serving 100K+ 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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- 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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Problem Solving & DSA
Key Highlights
- 5000+ Problems Solved Across 10+ Platforms
- 1400+ 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
Technical Skills
AI/ML
LLMsRAGAIOpsMCPLangChainOpenAI APIPyTorchVector DatabasesPrompt Engineeringscikit-learn
Frontend Development
Next.jsReactTailwindCSSReduxReact QueryCSSHTMLSSRCSRHybrid RenderingBootstrap
Backend Development
Node.jsFastAPIExpressDjango.NET
Cloud & DevOps
DockerKubernetesAWSAzureTerraformCI/CDGitHub ActionsGitLab ActionsJenkinsGrafanaPrometheus
Databases
MongoDBPostgreSQLMySQLRedisFirebaseChromaDBVector DatabasesVector Search
Programming Languages
PythonTypeScriptJavaScriptSQLJavaC++Bash
Tools
GitGitHubGitHub CopilotVS CodePyCharmLinuxIntelliJ IDEAPostmanFigmaSeleniumScrapy
Education
Sage University Indore
B.Tech in Computer Science
2020 – 2024 · MP, India
CGPA: 8.5/10
