---
title: "Soham Chaudhari - Artificial Intelligence Intern in Mumbai Metropolitan Region | Tal"
description: "Meet Soham Chaudhari, AI Developer | Generative AI & LLM Systems | RAG, LangGraph, PyTorch, Multimodal AI | Building Production-Grade AI Applications | AI Intern @ FLIP based in Mumbai Metropolitan Region."
image: "https://tal.club/meet/soham-12/share-image"
---

# Soham Chaudhari on Tal

## Headline

AI Developer \| Generative AI & LLM Systems \| RAG, LangGraph, PyTorch, Multimodal AI \| Building Production\-Grade AI Applications \| AI Intern @ FLIP

## About

I build production\-grade AI systems that solve real problems — not demos, not prototypes\. As an AI Engineer, I specialize in LLM applications, RAG pipelines, AI agents, and multimodal systems\. I care deeply about reliability, cost efficiency, and systems that actually work at scale\. At FLIP, I engineered an 11\-node LangGraph orchestration pipeline with hybrid retrieval across 9,700\+ credit card offers, cutting token consumption by 60–70% through smart memory injection\. At UNLAWC, I built Legal\-Tech AI systems that reduced inference costs by \~80% and improved retrieval accuracy by 60% using LangChain, Pinecone, and evaluation\-driven iteration\. Core stack: LLMs · RAG · LangChain & LangGraph · Vector Databases · PyTorch · FastAPI & Node\.js · AWS & Docker · Multimodal AI Currently exploring: Agentic AI systems, LLM evaluation frameworks, efficient inference & quantization, and advanced multimodal architectures\. Open to: AI Engineer · ML Engineer · Generative AI Engineer · Applied AI · AI Infrastructure roles\.

## Location

Mumbai Metropolitan Region

## Work experience

Artificial Intelligence Intern at FLIP, Feb 2026 \- Present · 5 mos, Remote, \- Engineered an 11\-node LangGraph orchestration pipeline enabling hybrid retrieval across 9,700\+ credit card offers, combining semantic search, SQL filtering, vector embeddings, and web fallback for high\-accuracy recommendations\. \- Designed a PostgreSQL\-backed semantic memory layer with async session storage and feedback APIs, cutting token consumption by 60–70% through optimized context injection\. \- Deployed and maintained scalable AI backend infrastructure on AWS EC2 using Docker Compose, connection pooling, API rate limiting, and health monitoring — ensuring high availability and sub\-200ms response times\. \- Optimized retrieval and inference workflows across the full AI stack, enabling production\-scale performance with low latency and fault tolerance\.

AI Developer at UNLAWC, Sep 2025 \- Apr 2026 · 8 mos, Mumbai, Maharashtra, India, \- Architected RAG pipelines using LangChain and Pinecone with reranking and self\-correction workflows, improving retrieval accuracy by 60% and significantly reducing LLM hallucinations in legal document Q&A\. \- Reduced AI inference costs by \~80% through optimized retrieval strategies, efficient prompt design, and streamlined deployment — without sacrificing output quality\. \- Improved LLM response reliability by 31% via structured outputs, evaluation\-driven iteration, and systematic prompt engineering on legal domain tasks\. \- Automated end\-to\-end legal document analysis workflows, cutting manual review effort by \~50% and enabling faster turnaround for legal teams\.

Artificial Intelligence Engineer at UNLAWC, Sep 2025 \- Feb 2026 · 6 mos, Deployed end\-to\-end Legal Operations AI agents using LLMs, reducing manual document review time by 50%\. Optimized LLM performance through prompt engineering, improving contextual accuracy by 31%\. Architected RAG pipelines with Mistral AI, LangChain, and Pinecone vector database, increasing retrieval accuracy by 60% and eliminating hallucinations\.

SDE Intern at SR Counselling India, Dec 2024 \- Sep 2025 · 10 mos, Mumbai, Maharashtra, India, \- Built and deployed a production ML recommendation engine processing 10,000\+ daily interactions at 95% model accuracy, directly increasing user engagement by 35%\. \- Developed 15\+ REST APIs with Node\.js and Express\.js, supporting AI workflows and handling 5,000\+ concurrent requests with stable performance under load\. \- Designed an AI\-powered VISA training system that reduced operational training costs by \~90%, replacing manual processes with an automated, scalable pipeline\. \- Built scalable React\.js admin dashboards with reusable components, reducing page load times by 60%, and improved Flutter mobile app performance, boosting retention metrics by 35%\.

## Education

Bachelor of Engineering, Computer Engineering, Vivekanand Education Society's Institute Of Technology

[View the original profile](https://tal.club/meet/soham-12)

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