AI / GenAI Engineer focused on building production-ready ML and LLM systems
I work on applied AI problems involving LLMs, computer vision, and end-to-end ML and LLMOps systems, with hands-on experience taking models from experimentation to deployment.
- Retrieval-Augmented Generation (RAG) systems for large document analysis
- LLM pipelines with evaluation, fallback logic, and cost awareness
- Computer vision models for real-time video and image processing
- Production APIs using FastAPI, Docker, and cloud infrastructure
Languages: Python
ML / AI: PyTorch, OpenCV, LangChain, Transformers
Systems: FastAPI, Docker, AWS, GCP, Vector Databases
Focus areas: LLMOps, model evaluation, deployment, scalability
- Hope Bridge – Production-grade multi-model LLM system with monitoring and evaluation
- LLMOps Trading Bot – RAG-based financial document intelligence system, deployed on AWS EC2
- LLMOps Version Control Source Code Analyser System – RAG-based analysing system which clone, analyses and answers, deployed on GCP
- LLMOps Interview QA Generator – RAG-based QA generatore deployed on AWS
- (Masters Research Thesis)Emergency Sign Language Detection – Real-time CV system using CNN–LSTM