Sunanda Tata

Software Engineer | Backend & Distributed Systems | AI/LLM Engineering

About Me

Software Engineer with experience building production backend systems, distributed services, real-time data pipelines, and AI-powered applications.

My work has focused on the engineering problems that matter in production: latency, reliability, scalability, security, observability, and maintainability. I have built and optimized systems using Java, Spring Boot, Python, Kafka, Redis, PostgreSQL, Kubernetes, AWS, and Azure, while also developing practical AI applications with LangChain, RAG, vector search, PyTorch, and Hugging Face Transformers.

I’m especially interested in backend engineering, distributed systems, platform engineering, and applied AI, where strong software fundamentals meet high-impact production problems.

Engineering Focus

⚙️ Backend Engineering — Java, Spring Boot, Python, FastAPI, Node.js, REST APIs
🌐 Distributed Systems — Kafka, Redis, microservices, event-driven architecture
☁️ Cloud & Infrastructure — AWS, Azure, Docker, Kubernetes, Terraform, CI/CD
🤖 AI Engineering — LLMs, LangChain, RAG, vector search, Hugging Face, PyTorch
📊 Data Engineering — PostgreSQL, MySQL, MongoDB, Spark/PySpark, Apache Hudi
🔐 Production Reliability — OAuth 2.0, JWT, RBAC, Prometheus, Jaeger, automated testing

Featured Projects

🧠 ChronoMind — AI Memory Assistant

AI-powered memory retrieval system that combines semantic search with graph relationships to reconstruct contextual timelines from stored information.

Built FastAPI services around LangChain-powered RAG workflows.
Combined Qdrant vector search with Neo4j graph relationships for hybrid retrieval.
Used PostgreSQL for structured persistence across a multi-thousand-entry corpus.
Improved contextual retrieval quality compared with flat semantic search by combining graph and vector relationships.

Tech: Python FastAPI LangChain Neo4j Qdrant OpenAI PostgreSQL

🚗 Ride Matching and ETA Engine

Event-driven backend prototype for real-time driver matching and ETA estimation.

Built matching services with Java and Spring Boot.
Used Kafka to process driver and rider location events asynchronously.
Applied Redis Geo and geospatial PostgreSQL queries to rank nearby drivers.
Deployed the system on Kubernetes with Redis caching for low-latency matching workflows.

Tech: Java Spring Boot Kafka Redis Geo Kubernetes PostgreSQL