Mechanical undergrad who got into ML and never really looked back.
I build things at the intersection of AI/ML and software — mostly backend-heavy, sometimes full-stack.

Languages

AI engineering

RAG / vector

Eval / observability

Databases

Backend / infra

Tools & services

Frontend

Full-stack RAG app for storing and chatting with documents and long-form knowledge. Built the document ingestion and chunking pipeline, embedding and vector search workflow, contextual retrieval system, FastAPI backend APIs, and a thread-style frontend chat interface. Focused mainly on retrieval quality, clean backend structure, and scalable AI workflows.

Compliance-focused AI system for querying and validating company documents. Worked on PDF parsing and preprocessing, retrieval pipelines for policy lookup, structured LLM outputs, backend evaluation logic, and compliance workflow APIs. Built to make AI outputs more traceable and reliable instead of just generating text.

Platform for learning LeetCode and DSA problems with an AI coach. Built guided problem solving, AI-generated hints and explanations, step-by-step learning flow, problem tracking, and an interactive frontend. Built to make DSA practice feel more structured instead of randomly grinding problems.

ML project focused on predicting industrial equipment failures from sensor data. Worked on preprocessing and cleaning sensor datasets, feature engineering, training classification models, model evaluation and comparison, and visualisation of maintenance insights. Good place where the mechanical background and applied ML actually connected.

Open to AI/ML engineering and data science roles — especially anything where models actually ship to users.