---
title: "Hari Chenna - Senior Software Engineer (Contract – Client: Toku) in Hyderabad | Tal"
description: "Meet Hari Chenna, Full-Stack & AI/Backend Engineer | Node.js, TypeScript, React | LLM/RAG Systems, vLLM, Kafka based in Hyderabad. AI stack includes claude, github copilot, and langchain. Explore public work links including github and leetcode."
image: "https://tal.club/meet/hari-chenna/share-image"
---

# Hari Chenna on Tal

## Headline

Full\-Stack & AI/Backend Engineer \| Node\.js, TypeScript, React \| LLM/RAG Systems, vLLM, Kafka

## About

Software engineer with 3\.5\+ years building production fintech and AI\-powered systems — from React/Next\.js interfaces down to Node\.js/TypeScript backends, event\-driven architectures, and LLM/RAG pipelines\. I've doubled LLM\-serving throughput through vLLM optimization, cut regression testing effort by 70% by building an automated testing tool, and reduced documentation lookup time by \~70% with a production RAG assistant — all within enterprise banking and fintech environments requiring ACID\-compliant, concurrency\-safe systems\. Comfortable owning work end\-to\-end: API and schema design, event\-driven microservices \(Kafka\), frontend interfaces \(React/Next\.js\), and production AI infrastructure \(RAG, embeddings, LLM\-as\-judge evaluation, inference optimization\)\. Open to full\-stack, backend, and AI/LLM engineering roles\.

## Location

Hyderabad

## AI stack

claude

github copilot

langchain

ollama

openai

vLLM

## Work experience

Senior Software Engineer \(Contract – Client: Toku\) at IT SPEAR, Mar 2026 \- Jul 2026 · 5 mos, Remote, Refactored STT/LLM services using a factory architecture, removing unused API, WebSocket, and database components, reducing codebase size by 10%\+ and improving maintainability and testability\. Rearchitected STT/LLM services for the Makimoto AI open\-source initiative by separating application logic from model\-serving infrastructure, enabling independent deployments and reducing deployment time by 30%\. Migrated LLM serving from Hugging Face Llama to vLLM, replacing sequential inference with continuous batching to double throughput and reduce latency by 30% under concurrent load, with an additional 12% gain from prefix\-cache optimization\. Built an LLM\-as\-judge evaluation framework to score response accuracy and relevance against reference answers, guiding iterative prompt and model improvements and cutting manual QA review effort by \~40%\. Migrated STT inference from whisper\-large\-v3 to Faster\-Whisper with CTranslate2, reducing inference latency by \~50%\. Implemented Prometheus/Grafana observability across STT and LLM services, tracking inference latency, throughput, resource utilization, and service health to identify performance bottlenecks\.

Product Developer at EdgeVerve, Jan 2025 \- Feb 2026 · 1 yr 2 mos, Hyderabad, Telangana, India · Hybrid, Designed and developed a Commitment Fee Engine with a Node\.js/Express backend and React\-based configuration interface, supporting 4 fee types and using Kafka\-driven events to synchronize fee data across a separate database while maintaining transactional consistency\. Built full\-stack Loan Split & Merge functionality with a Node\.js backend and React frontend, using optimistic locking to eliminate race conditions during concurrent operations and ensure ACID\-compliant data consistency\. Built ATT \(Automated Testing Tool\), an automated regression\-testing tool that cut regression testing effort by 70%\. Resolved 150\+ integration defects across distributed systems Mentored 4 junior developers through task planning, design reviews, and code reviews, reducing average onboarding\-to\-productivity time from 8 weeks to 5 weeks\.

Associate product developer at EdgeVerve, Dec 2022 \- Jan 2025 · 2 yrs 2 mos, Hyderabad, Telangana, India · Hybrid, Implemented role\-based session authentication and authorization across 4 core banking APIs, strengthening access control for enterprise lending workflows\. Developed a RAG Knowledge Retrieval Assistant \(Python, LangChain, FastAPI, pgvector\) to query banking API documentation and policy manuals, cutting average documentation lookup time by \~70% for internal teams\. Built a PDF processing and embedding pipeline \(chunking with overlap, pgvector storage\) for semantic retrieval via cosine similarity, grounding LLM responses with citation guardrails and achieving \~85% Precision@5 on internal documentation retrieval\.

## Education

Bachelor of Technology \- BTech, Electrical and Electronics Engineering, SAI RAJESWARI INSTITUTE OF TECHNOLOGY AND SCIENCES, LINGAPURAM

Diploma of Education, Electrical and Electronics Engineering, Government Polytechnic College, Proddatur

## Public links

- github: https://github\.com/Chari143
- leetcode: https://leetcode\.com/Chari143

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

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