Software Engineering · Backend & Distributed Systems · Applied AI
Go · Docker · Kafka · Next.js · FastAPI · WebSockets · Observability
I am a curious and driven software engineer with a strong focus on building backend systems that are reliable, scalable, and production‑ready.
My core interests lie across 3 broad domains: Backend Engineering, Distributed Systems, and Applied AI. I enjoy working on problems that help me understand why systems fail in real environments. Issues like race conditions, memory pressure, poor service isolation, and lack of observability are some important aspects I consider before starting to design and implement software.
Good systems are built by anticipating failure early, as a result I try and implement clean and understandable architectures wherever possible.
Alongside project‑based learning, I am actively working on improving my problem‑solving skills via solving Data Structures & Algorithms questions regularly, and maintain a solid understanding of core Computer Science fundamentals such as Object‑Oriented Programming, Databases, and Computer Networks. This balance helps me reason clearly both at the code level and at the system level.
- A strong sense of owning up to what I do, and taking up responsibilty with a continuous‑learning mindset.
- Comfort working across different tech stacks, with a primary inclination towards MERN stack development in web development.
- A solid competitive programming background using Java, which naturally led me to explore Spring and its framworks.
- Hands-On experience applying Python‑based AI workflows, using libraries such as NumPy and Pandas, and frameworks like FastAPI for model evaluation and experimentation.
- Experience with working on complex topics for AI/ML research with papers published in relevant IEEE conferences.
- Ongoing work with LLM fine‑tuning, and SpringBoot microservices architectures, with a primary focus on reducing latency and improving scalability.
Stack: Next.js · FastAPI · Python · OAuth2 · Docker
A full‑stack platform that replaces manual sprint tracking with automated insights. It analyzes team activity to surface burnout risk, velocity trends, and operational signals for managers.
Why this project matters
- Designed a clean 3‑tier architecture (frontend, API, AI service layer).
- Implemented strict Role-Based Access(RBAC) with OAuth2 + JWT (Org / Manager / Employee).
- Helped managers visualise a dashboard which helped reduce burnout in Agile teams.
Engineering details
- Solved time‑series aggregation issues for reliable analytics.
- Split frontend and backend containers to enable independent scaling.
Stack: Apache Kafka · PostgreSQL · Docker · Prometheus · Grafana
A microservices‑based analytics system built around Kafka for asynchronous message-ingestion and processing.
System design
- Producer → Kafka broker → consumer architecture.
- Persistent storage with PostgreSQL.
- End‑to‑end metrics and dashboards.
Production lessons applied
- Fixed service startup race conditions using Docker health checks.
- Prevented Kafka crashes by tuning JVM heap limits.
- Resolved container networking and metrics scraping issues.
| Project | Focus | Takeaway |
|---|---|---|
| IoT Log Anomaly Detection | NLP · Unsupervised Learning | Detected anomalies in system logs using small language models. |
| Benchmarking XgBoost and Bi-LSTM for stock analysis | ML · Explainability · Time-Series Forecasting | Built an interpretable ML pipeline with measurable performance. |
Languages Java, Go, JavaScript, Python, PHP
Backend & Systems FastAPI, Node.js, Docker, Apache Kafka, RESTful APIs, Microservices, Redis Streams
Databases PostgreSQL, MongoDB, MySQL, SQLite
Frameworks Express, Spring Boot, Next.js, PyTorch, TensorFlow, XGBoost, Bi‑LSTM
Libraries React, NumPy, Pandas, SQLAlchemy (ORM), Scikit‑Learn, Matplotlib
Observability & Tooling Prometheus, Grafana, Git, Postman