My initial dive into software engineering started with competitive programming, but I eventually realized that solving isolated algorithmic puzzles wasn't my strongest suit. I pivoted to AI/ML and found what actually clicks for me: building practical, intelligent applications.
Today, my GitHub is focused on the intersection of AI and software engineering. I am less interested in just training models in Jupyter notebooks, and more interested in figuring out how to serve them, wrap them in clean APIs, and build the backend infrastructure to make them usable.
I am currently working as a trainee, heavily focused on applied Generative AI and backend development. Right now, my commits usually involve:
- Applied AI: Building RAG pipelines, multi-agent systems, and OCR pipelines using tools like LangChain, CrewAI, and PyTorch.
- Backend Integration: Writing Python microservices (FastAPI / Flask) to connect AI models to web and mobile frontends.
- Databases & Deployment: Managing data with PostgreSQL and Vector DBs, and using Docker to keep my environments clean and deployable.
To become a better backend engineer and move beyond just Python scripts, I am actively exploring:
- Golang: Learning Go to build lighter, faster, and more concurrent backend services.
- System Design: Studying how larger distributed systems and cloud infrastructures are put together.
Languages & Tools
Python • C/C++ • Go (Learning)
FastAPI • Docker • PostgreSQL • Git
LangChain • PyTorch • Transformers
Building software where AI is one part of a larger system.