---
title: "Prateek Janagouda - Software Engineer Trainee (machine learning) in Bangalore Urban | Tal"
description: "Meet Prateek Janagouda, AI/ML Engineer | LLM Deployment · RAG Systems · Agents | Python · vLLM · Qdrant | Open to full-time roles based in Bangalore Urban. AI stack includes claude code, claude, and codex. Explore public work links including github and prateekjanagouda.github.io."
image: "https://tal.club/meet/prateek-janagouda/share-image"
---

# Prateek Janagouda on Tal

## Headline

AI/ML Engineer \| LLM Deployment · RAG Systems · Agents \| Python · vLLM · Qdrant \| Open to full\-time roles

## About

AI/ML Engineer with 1\+ year building LLM systems at Synchronoss Technologies\. What I've worked on: → Migrated LLM inference from Ollama to vLLM — benchmarked 16 concurrent requests vs Ollama's bottleneck → Engineered a RAG pipeline over 7M\+ code chunks using Qdrant, BGE\-large embeddings and AST\-aware chunking → Built PyTestGen — AI unit test generator using local LLaMA \(https://github\.com/PrateekJanagouda/pytestgen\) → Built MiniRAG — complete RAG API from scratch, no LangChain \(https://github\.com/PrateekJanagouda/minirag\) Passionate about agents, RAG systems and LLM inference optimization\. Open to full\-time ML/AI Engineer roles in Bangalore\. GitHub: https://github\.com/PrateekJanagouda

## Location

Bangalore Urban

## AI stack

claude code

claude

codex

deepmind

gemini

hugging face

## Work experience

Software Engineer Trainee \(machine learning\) at Synchronoss Technologies, Nov 2025 \- Present · 1 yr, Bengaluru, Karnataka, India, Working on production LLM systems — retrieval, agents, and inference\. • Built an internal agentic RAG assistant over a large Java microservice codebase\. It answers plain\-English engineering questions and returns answers backed by citations to the source, and is used by the engineering team day to day\. • Designed it around two stores rather than one: a relational store for exact structural facts \(symbols, call graph, service boundaries\) and a vector store for semantic search\. Structural questions like "what calls this" are graph queries — similarity search answers those confidently and wrongly\. • Combined code\-specialised and text\-specialised embedding models with a cross\-encoder reranker, and added a bytecode\-analysis pass on top of tree\-sitter so the call graph resolves across repository boundaries, which source parsing alone cannot do\. • Built the evaluation harness: it generates questions from the index itself and scores answers against cited evidence, with a citation validator and confidence scoring, so retrieval quality is measured rather than assumed\. • Built a second agent for configuration management that constrains the model to emit only RFC 6902 JSON Patch operations against a fixed schema, grounded in a real diff, instead of letting it rewrite YAML freely\. Every generated change is schema\-validated and dry\-run before it can be applied, with rollback\. Tech: Python, FastAPI, vLLM, Qdrant, PostgreSQL, tree\-sitter, Helm, Docker

Machine Learning Intern at Synchronoss Technologies, Oct 2024 \- Oct 2025 · 1 yr 1 mo, Bengaluru, • Evaluated open\-source LLMs for Java test generation, then load\-tested the serving layer from 1 to 2,000 concurrent users\. Ollama dropped every request at 20 concurrent; moving serving to vLLM held 1,000 concurrent at 100% success and \~790 tok/s using continuous batching\. • Benchmarked open\-source code models \(Llama, StarCoder, CodeGen\) for JUnit test generation — set each up locally and evaluated output quality to pick the best performer\. Integrated the Continue plugin with IntelliJ so developers could generate tests inside their editor\. • Researched and benchmarked open\-source face\-recognition models, selected FaceNet, and integrated it through the DeepFace library, tuning it for face\-clustering performance\. • Built a media\-processing pipeline that clusters faces across video and classifies expressions in real time using DeepFace's emotion\-detection module\. Tech: Python, Llama, StarCoder, CodeGen, vLLM, Ollama, DeepFace, FaceNet, Continue, IntelliJ

## Education

Bachelor of Engineering \- BE, Artificial Intelligence and data science, Visvesvaraya Technological University

mahesh pu college

## Public links

- github: https://github\.com/PrateekJanagouda
- prateekjanagouda\.github\.io: https://prateekjanagouda\.github\.io/

[View the original profile](https://tal.club/meet/prateek-janagouda)

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