I’m a BCA (Hons.) Artificial Intelligence & Data Science student at Graphic Era Deemed to be University, building toward a career in AI engineering.

My favorite work sits at the boundary between intelligence and action: a camera gesture moving a Unity character, a retrieval system grounding an answer in private documents, or an agent turning an unstructured request into an auditable workflow.

I learn by shipping complete systems—not only notebooks. My strongest repositories include tests, CI, architecture documentation, security and privacy notes, Docker support where it helps, and interfaces that make the underlying intelligence usable.

Current direction: reliable AI products, agentic automation, computer vision, and full-stack systems with transparent behavior and responsible boundaries.

A webcam-only computer-vision bridge that turns hand movement, pinches, and two-palm gestures into a complete Unity cinematic experience. Python handles MediaPipe perception and debouncing; a WebSocket protocol carries typed events; Unity controls movement, rain, lighting, candles, curtains, audio, and camera choreography.

Python · OpenCV · MediaPipe · WebSocket · Unity · Human-Computer Interaction

Engineering proof: 34 Python tests · headless Unity compile verified · privacy-first local processing · procedural scene · CI · Docker bridge · protocol and architecture docs

What I built: the Python gesture pipeline, stability and debouncing logic, typed WebSocket boundary, and Unity-side event integration that turns perception into a complete interactive scene.

The live portfolio adds filterable project categories, an engineering journey, verified learning highlights, and direct paths into the repositories above.

The six projects above are the work I recommend reviewing first. The broader portfolio below includes supporting builds and learning experiments that document how my engineering skills have developed.

My project work is supported by 19 verified Coursera course certificates and one specialization across applied AI and responsible engineering.

Verify all credentials on LinkedIn →

• RAG evaluation, retrieval quality, and citation reliability
• Agent safety, approval boundaries, observability, and auditable tool use
• Cloud deployment, production APIs, and end-to-end system reliability
• Computer-vision interaction patterns that feel natural to real users

My strongest portfolio projects are built to answer more than “does the demo run?”

clear problem
    → modular implementation
    → automated tests + lint
    → architecture and API documentation
    → privacy, safety, and limitation notes
    → CI + reproducible setup
    → interface a reviewer can actually use

That standard reflects how I want to grow: ambitious about what software can do, honest about its limits, and persistent about the unglamorous work that makes it dependable.

ask why → build the smallest real version → test the risky assumptions
   ↑                                              ↓
share what I learned ← document the trade-offs ← improve what failed

I value visible progress over perfect plans, strong fundamentals over fashionable vocabulary, and the patience to stay with integration problems until the complete system works.

I’m open to AI engineering internships, collaborative projects, research-minded conversations, and opportunities to build useful systems with thoughtful people.

_{Curious enough to explore. Disciplined enough to ship.}