I build AI systems and the backend infrastructure that runs them - LLM pipelines, RAG, agents & distributed services.
I’m an AI & ML undergrad at Dayananda Sagar College of Engineering, Bengaluru - class of 2027.
My work lives where backend engineering meets applied AI: agentic workflows, retrieval pipelines, and the distributed systems that serve them at scale. I care about throughput, reliability and clean boundaries as much as the model on top.
Python
Go
C / C++
LangChain✦LangGraph✦RAG✦MCP
PyTorch✦LLMs
Django
FastAPI
React⇄gRPC
Docker
Kafka
Celery
Linux
Git
PostgreSQL
MongoDB
Redis▦ChromaDBAI-driven interview platform on agentic workflows for scoring & reporting - an end-to-end pipeline from ATS screening through interview conduction to evaluation.
Clinical flow metrics from video instead of dedicated uroflowmeter hardware. A deterministic OpenCV pipeline segments the urinary stream frame by frame and normalises the curve against a measured volume to report Qmax, flow time and hesitancy. taskiq workers keep the pipeline off the request path, with live progress streamed over SSE.
Medical records with a consent model built for the moment the patient can’t speak. A QR emergency card lets a bystander report an accident; the alert routes to a nominated contact who can admit one named doctor for a bounded window. Go/Gin API over Postgres, queued mail with a retry ladder, and an isolated Python service running a CLIP + linear-probe accident classifier.
An AI-based feedback system used by students and faculty. Built a RAG-based analytics microservice on FAISS & Ollama for automated insights, with containerised dashboards via Docker Compose - fully local, no data leaving the box.
Go-based webhook listeners and gRPC microservices for scalable processing - automated PR analysis & release-note generation with real-time commit and release dashboards.
GSoC ’26 · Open Source
Backend · AI Engineer
