---
title: "Revanth Vermareddy - AI Engineer / Member of Technical Staff - 3 (MTS-3) in Bengaluru | Tal"
description: "Meet Revanth Vermareddy, Senior AI Engineer (MTS-3) at NetApp | NITW Alum | Ex-Fidelity Investments | Distributed Systems & AI Data Platforms | Go, Python, AWS, K8s, gRPC, MongoDB, Kafka | LLM | Fine Tuning | LangGraph | Harness Engineering based in Bengaluru. AI stack includes claude code, cursor,..."
image: "https://tal.club/meet/revanth-vermareddy/share-image"
---

# Revanth Vermareddy on Tal

## Headline

Senior AI Engineer \(MTS\-3\) at NetApp \| NITW Alum \| Ex\-Fidelity Investments \| Distributed Systems & AI Data Platforms \| Go, Python, AWS, K8s, gRPC, MongoDB, Kafka \| LLM \| Fine Tuning \| LangGraph \| Harness Engineering

## About

I bridge the gap between massive enterprise data estates and actionable, production\-grade AI\. As a Senior AI Engineer, my specialization is building the core distributed systems, multimodal retrieval \(RAG\) pipelines, and optimized inference infrastructure required to run Large Language Models reliably and at scale\. I thrive on turning the chaos of terabytes of unstructured data into seamless, secure, intelligent user experiences\. My journey spans across fast\-paced startup ecosystems and massive financial frameworks\. Currently, at NetApp, I contribute to the AI Data Engine, architecting distributed content processing systems in Go and building production Kubernetes operators to automate AI inference on GPU\-accelerated clusters\. Before this, I engineered LangGraph\-based agentic workflows and credit risk scoring engines at Beem, and built massive 30TB/day big data pipelines at Fidelity Investments\. Across these roles, my technical execution centers on: Languages & Frameworks: Go, Python, Java, FastAPI, Spring Boot AI/ML Infrastructure: vLLM, TensorRT\-LLM, NVIDIA GPU Operator, Milvus \(Vector DB\), LangGraph, SLM Fine\-Tuning Cloud & Distributed Systems: Kubernetes \(Kubebuilder\), AWS \(EKS, Lambda\), Apache Kafka, Apache Spark, Docker I’m always excited to connect with fellow engineers, AI practitioners, and tech leaders who are building the future of intelligent infrastructure\. Whether you want to discuss distributed systems optimization, LLM orchestration, or just talk shop about Go and Kubernetes—let’s connect\!

## Location

Bengaluru

## AI stack

claude code

cursor

github copilot

langgraph

Google ADK

vLLM

## Work experience

AI Engineer / Member of Technical Staff \- 3 \(MTS\-3\) at NetApp, Jan 2025 \- Present · 1 yr 8 mos, Bangalore Urban, Karnataka, India, Building NetApp's AI Data Engine — an enterprise platform delivering LLM\-powered intelligence over terabytes of unstructured ONTAP data for 1,000\+ enterprise customers\. \- Architected a distributed content processing engine in Go powering multi\-tenant document ingestion, classification, PII anonymization, and embedding generation at production scale\. \- Engineered a multimodal RAG pipeline — semantic chunking, vector indexing \(Milvus\), similarity search, and cross\-encoder re\-ranking — over TBs of enterprise unstructured data\. \- Drove VLM inference optimization via vLLM and TensorRT\-LLM, accelerating GPU throughput and reducing inference latency for multimodal workloads in production\. \- Built a production Kubernetes operator \(Kubebuilder \+ NVIDIA GPU Operator\) automating lifecycle management and scheduling of AI inference workloads on GPU\-accelerated clusters\. 🛠 Go · Python · Kubernetes · Kubebuilder · vLLM · TensorRT\-LLM · NVIDIA GPU Operator · Milvus · RAG · LLMs · Prometheus · Grafana

Sr\. Software and Data Engineer \(Credit Risk and Data Science\) at Beem, Jun 2022 \- Jan 2025 · 2 yrs 8 mos, San Francisco, California, United States, Built the AI and risk intelligence layer at Beem — designing LLM\-powered agentic systems and credit scoring infrastructure driving real\-time financial decisions for millions of users\. \- Engineered LangGraph\-based agentic AI workflows for the risk and credit data science team, orchestrating multi\-step LLM reasoning pipelines for automated financial intelligence\. \- Built and deployed an XGBoost underwriting API and Beem Index risk scoring system, enabling real\-time credit decisioning at production scale\. \- Architected Beem's BFF \(Better Financial Feed\) — a personalized financial insights engine delivering millions of tailored recommendations, inspired by social feed ranking systems\. \- Designed and scaled large\-scale push notification and newsletter infrastructure, driving user engagement across Beem's growing customer base\. 🛠 Python · LangGraph · AI Agents · LLM Orchestration · XGBoost · FastAPI · Risk Scoring · Credit Underwriting · LLMs

Software Engineer at Fidelity Investments, Dec 2019 \- Jun 2022 · 2 y 6 mo, India, Engineered Fidelity's Data Acquisition and Analytics Framework \(DAAF\) — a 30TB/day big data pipeline processing financial data from 450\+ vendors to power core business analytics\. Recipient of the Chapter Champion Eureka Innovation Award 2021\. \- Built an NLP\-powered automation \(SpaCy \+ AWS Lambda\) that eliminated manual config onboarding for 450\+ vendor files — converting 2,400\+ engineering hours into under a minute of runtime, saving \~$187K in BU costs\. \- Developed Spring Boot APIs providing controlled access to distributed big data infrastructure, containerized and deployed on Kubernetes at production scale\. \- Led tribe\-wide OAuth vendor migration as part of cloud modernization — first engineer to implement and document the pattern, subsequently adopted across all teams\. \- Deployed Bloomberg Live API integration on EKS, enabling real\-time financial market data access for downstream analytics\. 🛠 Python · Java · Apache Spark · Hadoop · Hive · AWS Lambda · Kubernetes · NLP · SpaCy · Spring Boot · EKS

Executive Graduate Trainee at Fidelity Investments, Aug 2019 \- Dec 2019 · 4 mo, Bengaluru, Karnataka, India, Joined into Fidelity's LEAP program \- a four\-month enriched onboarding program that included technology, business, leadership and personal development training\. I got into the Software Engineering track that included general training and an opportunity to work on a business project which tested analytical problem solving with strong coding capabilities\. Technologies learnt: Java, Springboot, Angular, Html, JS, CSS and Restful services\. Worked on a project which automates creating a new Financial Security data into a Master DB \- used NLP to analyze unstructured data of an e\-mail to gather the Financial Security information, whole project is built on a scalable and serverless architecture deployed in AWS\. Implemented an alternative approach for the above automation by creating an Angular Chatbot that integrates with AWS Lex for applying the NLP\.

## Education

Bachelor of Technology \- BTech, Electronics and Communication Engineering, National Institute of Technology warangal

## Public links

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

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

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