AI Engineer | MSc AIML @ IIIT Lucknow | Currently @ Evomaton
I bridge the gap between complex machine learning research and scalable production systems. I specialize in building agentic workflows, rigorously evaluating RAG pipelines, and engineering the backend infrastructure (FastAPI, Celery, AWS) to serve AI efficiently.
- Architecture: Engineered a multi-agent platform using LangGraph and Groq (LLaMA 3.3 70B) with a Planner agent routing queries for RAG and tool execution.
- Backend & Infra: Built robust async ingestion via Celery + Redis and real-time token streaming (SSE) to a React frontend. Deployed end-to-end on AWS EC2/RDS and containerized with Docker.
UGSA-TSGAN: Tumor-Attentive GAN (M.Sc. Thesis)
- Research: Designed a novel Uncertainty-Guided Soft-Attention Block (UG-SAB) using Monte Carlo Dropout to synthesize dynamic contrast-enhanced MRIs.
- Impact: Cut annotation needs by 40% via pseudo-labelling and achieved +6.5% Global SSIM over fully-supervised baselines.
- Data: Meticulously curated and preprocessed a paired dataset from 239 patients in the I-SPY1 dataset.
- Evaluation & Metrics: Implemented an LLM-as-a-judge (RAGAS) evaluation pipeline scoring faithfulness and context precision. Achieved 92.4% retrieval precision@5 and 84.7% keyword accuracy.
- Tech Stack: LangChain, ChromaDB, HuggingFace embeddings, and fully offline inference via Ollama/LLaMA.
- Workflow: Automated end-to-end ecommerce creative generation using a 6-stage multi-agent LangGraph workflow.
- Engineering: Built FastAPI APIs with Celery + Redis for async job execution and CSV-based bulk processing.
- AI Systems: LangGraph, RAG, Prompt Engineering, LoRA/QLoRA, Agentic AI, Groq
- Deep Learning: PyTorch, GANs, ViT, CNNs, Hugging Face
- Backend & MLOps: FastAPI, Docker, Celery, Redis, AWS (EC2/RDS), W&B
- Languages: Python, SQL