GenAI Engineer | Full Stack Developer | Building AI-Powered Applications | PyTorch, Transformers | Meta Hackathon Finalist
I am a B.Tech student specializing in Artificial Intelligence and Cloud Computing, with a strong interest in building real-world AI-powered applications. My work focuses on combining Generative AI, Machine Learning, and Full Stack Development to create scalable and efficient systems. I have hands-on experience with deep learning models, reinforcement learning, and modern web technologies, allowing me to develop end-to-end intelligent applications. I have built projects such as an AI-based portfolio optimization system using LSTM and multi-agent reinforcement learning (PPO, DDPG, A2C), a multi-tenant SaaS platform with secure authentication and billing integration, and an offline voice AI system using Whisper for real-time transcription. I enjoy working on performance optimization and system design, and have improved system efficiency and latency significantly across multiple projects. Achievements: • Finalist – Meta PyTorch OpenEnv Hackathon (Top ~2.6% among 31,000+ teams) • Winner – IBM Day Hackathon (AI/ML Track) I am currently looking for opportunities in Machine Learning, AI Engineering, and Full Stack Development where I can contribute to building impactful, real-world solutions.
United University
9 mos · Prayagraj
Reinforcement Learning & Portfolio Optimization
Prerogative Group of Institutes (PGOI)
4 mos
MERN Stack
Prerogative Group of Institutes (PGOI)
09/2023 – 12/2023 · Remote
Built and shipped scalable full-stack applications using the MERN stack with modular backend architecture; designed secure RESTful APIs with JWT authentication and role-based access control; reduced backend response latency by 30–40% through query optimization and efficient data-access patterns; improved frontend load speed by approximately 25% through optimized rendering and state management.
Bachelor of Technology - BTech, Computer Science
Sep 2022 - Sep 2026
CGPA: 8.5; Prayagraj
Class 12th - CBSE
Jul 2020 - Jul 2021
Class 10th - CBSE
May 2018 - May 2019
I built an AI-powered portfolio optimization system that leverages deep learning and reinforcement learning to make real-time investment decisions on market data. Used LSTM + PPO/DDPG/A2C for dynamic allocation. Integrated FinBERT for sentiment analysis. Designed custom OpenAI Gym trading environment. Achieved stable convergence over 50K+ timesteps. Real-time inference (<120 ms) using FastAPI + React.
Built a production-grade GenAI platform using Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), agent tool-calling, and memory systems. Implemented FAISS-based vector search (1K+ chunks/user) for context-aware responses Developed multi-turn chat with real-time SSE streaming (<150ms latency) Integrated voice AI (Whisper + TTS) with ~50% latency optimization Designed full-stack system using FastAPI, React, PostgreSQL, Redis, and Docker
Built a production-grade AI platform with RAG, agent tool-calling, memory systems, voice AI, and SSE streaming with latency below 150ms. Implemented FAISS retrieval with 1K+ chunks per user and Redis plus vector memory, improving response quality by 30%+. Integrated Whisper and TTS with 50% latency optimization, using FastAPI, React, PostgreSQL, and Docker.
Built a Transformer and reinforcement learning system using PPO, DDPG, and A2C for portfolio optimization on real market data. Achieved stable reward of approximately 68+ with convergence over 50K+ timesteps, designed a custom trading environment with Sharpe ratio and cost modeling, and enabled real-time inference below 120ms via FastAPI and React.
Built a multi-tenant SaaS architecture supporting 100+ users with CRM workflows, automation, and analytics. Implemented RBAC authentication and Stripe billing, reducing manual operations by 40%+, and designed a scalable backend handling 1K+ API requests per day.