Jagadeesh Reddy Vanga

Research engineer building intelligent AI systems at scale

Specializing in LLM architectures, production ML, and systems that work in the real world.

Get in touch

I build AI systems that bridge research and production.

Currently at Vanderbilt University's College of Connected Computing, building production GenAI systems and training the next generation of AI engineers.

Clarity over complexity

Simple systems that scale beat complex ones that break.

Production-first

Research without deployment is just theory.

Depth over breadth

Deep expertise in fewer things beats surface knowledge of many.

Scale matters

Building for 10 users vs 250K users are different problems.

Semantic Product Search Engine

Production ML

Built and deployed a vector-based semantic search engine processing 250,000+ products for an enterprise e-commerce platform. Achieved 90%+ accuracy through custom embedding models and intelligent ranking algorithms.

Impact

35% increase in user engagement, 60% reduction in search latency through Redis caching architecture.

Approach

Hybrid retrieval combining semantic embeddings with traditional search. Custom ranking models tuned for domain-specific relevance. Distributed architecture with auto-scaling on AWS.

Stack

Python, FAISS, Redis, PostgreSQL, AWS Lambda, Docker

View project �

DataPulse

Best in Show

AI-powered customer segmentation platform combining Snowflake data pipelines with LLM-driven insights. Winner of "Best in Show" award for innovative application of data analytics and generative AI.

Challenge

Traditional segmentation lacks context. How do you make data actionable without requiring SQL expertise?

Solution

Built natural language interface over Snowflake using Gemma LLM. Automated segment generation with AI-driven insights. Real-time analytics pipeline processing millions of customer records.

Stack

Snowflake, Gemma LLM, Python, React, ETL pipelines

View project �

AI Interview Agent

GenAI

Multimodal interview simulation system combining Ollama LLM with Whisper speech recognition. Conducts natural voice conversations analyzing both content and delivery.

Technical depth

Real-time speech-to-text pipeline. Context-aware dialogue management. Preloaded with job descriptions and resume parsing for targeted questions.

Architecture

Local LLM inference with Ollama. Whisper for speech recognition. FastAPI backend with WebSocket for real-time interaction. Modular design for easy model swapping.

Stack

Ollama, Whisper, Python, FastAPI, WebSockets

View project �

TeleMedi

Healthcare AI

Telemedicine platform with Llama 3.1 integration for intelligent symptom analysis and diagnosis assistance. Bridges the gap between patients and medical professionals through AI-augmented consultations.

Constraints

Healthcare requires accuracy, privacy, and explainability. Can't sacrifice any for convenience.

Implementation

Local LLM deployment for privacy. Structured prompting for consistent medical reasoning. Integration with video consultation platform. Audit logging for compliance.

Stack

Llama 3.1, React, Flask, PostgreSQL, WebRTC

View project �

More projects and experiments on the full projects page

Sept 2025  Present

Generative AI Research Engineer

Vanderbilt University College of Connected Computing

Building GenAI software systems at Vanderbilt's College of Connected Computing. Architecting LLM-powered applications, deploying AI infrastructure on AWS, and creating comprehensive training programs for engineering teams.

May 2025  Aug 2025

Data Engineer

Haystream

Architected enterprise Snowflake data pipelines and ETL workflows. Optimized query performance by 40% through warehouse tuning and intelligent caching strategies.

Apr 2025  Jun 2025

Generative AI Engineer

Astoria AI

Built RAG pipelines with Pinecone vector database achieving 25% accuracy improvement. Deployed real-time AI agents with sub-second response times.

Jan 2024 – May 2024

Graduate Teaching Assistant

Stony Brook University

Supported course instruction and student learning in computer science graduate programs, providing academic guidance and technical assistance.

Oct 2021  Dec 2022

Software Engineer (Solutions & Machine Learning)

Skit

Spearheaded the development of AI-powered Voice Bots tailored for fintech clients, automating over 100,000+ customer service calls daily, streamlining operations, and improving customer experience. Collaborated cross-functionally with Delivery, Infra, and Product teams to design robust solutions for non-standard use cases. Built and optimized data pipelines and dashboards using PL/SQL and PostgreSQL in Metabase, uncovering key customer behavior insights. Developed and deployed RESTful APIs using Django/Python. Fine-tuned multilingual language models to boost voice bot accuracy, scalability, and personalization.

Feb 2021 – Sept 2021

AI Team Lead

Eunimart

Led AI team at Eunimart, ensuring 100% project success across eight diverse projects. Implemented event-driven messaging systems using Kafka/RabbitMQ for real-time data streaming. Developed cutting-edge AI solutions to optimize business processes and drive innovation. Collaborated with cross-functional teams to deliver impactful results and drive business growth.

Jun 2020 – Sept 2021

Software Developer

Eunimart

Developed a Similar Products Predictor using VGG16 & BERT, enhancing product recommendation accuracy. Implemented a Sales Prediction Model with XGBoost and Prophet, improving demand forecasting accuracy. Created an Image Optimizer service using OpenCV, reducing dependency on professional photo shoots.

Jun 2019 – Jun 2020

Intern

Eunimart

Designed and deployed an automated Web Scraper (Selenium) for e-commerce data collection, improving data quality by 30%. Gathered over 50 million product records to enhance market analysis and decision-making processes.

Building intelligent AI systems that scale.

Exploring LLM architecture patterns for production environments. Researching efficient prompt engineering techniques for domain-specific applications. Investigating RAG pipeline optimizations for enterprise data at scale.

Currently working on frameworks for deploying GenAI systems with reliability, observability, and cost-efficiency.