I'm an MCA student and software developer focused on building reliable backend and AI/ML systems.
My work spans Python backend development, distributed architecture, concurrency, machine learning, and human-in-the-loop AI systems. I'm particularly interested in the intersection of software engineering and machine learning — building the infrastructure and backend systems that make AI applications reliable, scalable, and useful in production.
My long-term direction is toward AI/ML Systems Engineering, with Python backend and distributed systems as my engineering foundation.
A C17 concurrent task scheduling library built around a bounded FIFO queue and fixed worker pool, with explicit lifecycle management, graceful shutdown, deterministic testing, and performance benchmarking.
C17 CMake CTest Multithreading Concurrency
Focus: worker pools · synchronization · bounded queues · lifecycle management · systems programming · performance
A distributed URL-shortening platform that goes beyond basic CRUD with load-balanced FastAPI replicas, Redis caching and rate limiting, PostgreSQL persistence, authentication, analytics, observability, and failure handling.
Python FastAPI PostgreSQL Redis Nginx Docker
Focus: distributed systems · caching · load balancing · API design · reliability · observability
A human-in-the-loop complaint investigation system for pharmaceutical quality workflows, supporting structured complaint extraction, risk assessment, duplicate detection, and RCA/CAPA recommendations.
Python FastAPI PostgreSQL LangGraph React TypeScript
Focus: AI workflows · structured LLM outputs · backend architecture · deterministic validation · human review
An AI-assisted refund review system combining deterministic eligibility rules, BM25 policy retrieval, and evidence-grounded AI explanations to help support agents evaluate refund requests.
Python FastAPI PostgreSQL React TypeScript Gemini Docker
Focus: deterministic decision logic · information retrieval · evidence-grounded AI · human-in-the-loop review
An ML decision-support system built on Walmart M5 data to identify slow-selling SKU-store combinations and evaluate conservative markdown scenarios.
Python scikit-learn FastAPI Streamlit Docker
Focus: machine learning · time-series features · model evaluation · feature engineering · decision support
| Area | Technologies |
|---|---|
| Languages | Python · C · SQL · TypeScript |
| Backend | FastAPI · Django · Django REST Framework · SQLAlchemy · REST APIs |
| Data | PostgreSQL · MongoDB · Redis |
| AI / ML | scikit-learn · LangGraph · RAG · BM25 · Feature Engineering · Model Evaluation |
| Systems | Concurrency · Multithreading · Worker Pools · Synchronization |
| Infrastructure | Docker · Nginx · Celery · Prometheus · Grafana |
| Frontend | React · TypeScript |
| Engineering | Git · GitHub Actions · Pytest · CTest · CMake |
I'm building toward AI/ML Systems Engineering — the intersection of machine learning and production software engineering.
Areas I'm currently developing deeper expertise in:
- scalable Python backend engineering
- distributed systems and reliability
- concurrency and systems programming
- model serving and AI application architecture
- retrieval and evaluation systems
- production-oriented ML/AI workflows
- cloud infrastructure and observability
I'm currently interested in opportunities in Python Backend Engineering, AI/ML Engineering, and Software Engineering, with a long-term focus on AI/ML Systems.