Who I Am
I'm a software and machine learning engineer working primarily on production systems — the kind that need to be shipped, maintained, and iterated on inside real products.
My background combines applied AI with strong systems engineering — from building ML-powered features in regulated SaaS environments to low-level game development with C++ and OpenGL.
What I Work On
Most of my experience is around applied ML in larger software environments: backend services, data pipelines, evaluation workflows, and the glue that holds them together. I'm comfortable moving across layers when needed, especially when things break.
Current focus areas:
- Production ML Systems: Building and refining end-to-end ML pipelines
- Automation & Tooling: Evaluation workflows and maintainability tooling
- Agentic Workflows: Experimenting with RAG and autonomous systems
How I Approach Problems
- Clear System Boundaries: I believe in well-defined interfaces and predictable behavior
- Debuggability First: I prefer solutions that are easy to understand over clever shortcuts
- Operational Impact: I think about deployment, monitoring, and maintenance from day one
- Incremental Iteration: Working software over perfect design
Technical Background
Languages
Python, Java, JavaScript, TypeScript, C++, SQL
ML & AI
PyTorch, TensorFlow, deep learning, NLP, model evaluation
Systems
REST APIs, modular design, state management, data pipelines
Tools
Git, Linux, MongoDB, Docker basics, cloud deployments
Experience
- Built and owned end-to-end ML-powered product modules within a modern SaaS EHR platform
- Designed modular Python-based ML services exposed via REST APIs
- Translated business requirements into deployable AI functionality
- Led Enterprise Data migration from heterogeneous environments
- Developed event-driven, real-time gameplay systems with strict performance constraints
- Designed UI/state transitions and debugged complex interactive systems
Education
Bachelor of Technology in Computer Science — GPA: 9.21/10
Achievements
- National level academic competition experience demonstrating strong analytical ability
- Hackathon experience delivering end-to-end ML and NLP applications under tight constraints