---
title: "Mihir Inamdar - Data Scientist in Bengaluru | Tal"
description: "Meet Mihir Inamdar, Agentic AI Engineer | RLHF · LangGraph · RAG · LLMs | Building AI agents in production | Qdrant Star ⭐ | IEEE Published based in Bengaluru."
image: "https://tal.club/meet/mihir-inamdar/share-image"
---

# Mihir Inamdar on Tal

## Headline

Agentic AI Engineer \| RLHF · LangGraph · RAG · LLMs \| Building AI agents in production \| Qdrant Star ⭐ \| IEEE Published

## About

Agentic AI Engineer building systems that actually run in production\. At Sutherland, I architect RLHF pipelines for Text\-to\-SQL from first principles — preference data collection, custom reward models, PPO policy optimization — serving 10K\+ daily transactions on AWS\. I also built a multi\-stage agentic inference system with quantized LLM serving \(4\-bit/8\-bit\) that cut latency 60% at F1 > 0\.95\. I'm a Qdrant Star: open\-source contributor to vector search and RAG infrastructure\. Built SQLRouter \(agentic Text\-to\-SQL with semantic caching\) and a sandboxed agent execution runtime from OS primitives\. Research background: CVIT, IIIT Hyderabad \(collaborated with Meta Reality Labs\)\. Published at IEEE ICETITE and Springer ICCCN\. IndoML 2022: 90\.8% at IIT Gandhinagar\. Stack: LangGraph · LangChain · RLHF · PPO · RAG · Pinecone · Qdrant · FastAPI · PyTorch · AWS · Docker Open to Agentic AI / LLM Engineering roles where AI is the product, not a bolt\-on\. DM me if you're building production agents and hitting retrieval or latency walls\.

## Location

Bengaluru

## Work experience

Data Scientist at Sutherland, Aug 2025 \- Present · 1 yr, Chennai, Tamil Nadu, India, Building production\-grade AI systems at the intersection of LLMs, RLHF, and agentic inference\. • Architected an end\-to\-end RLHF pipeline for Text\-to\-SQL from first principles — designed preference data collection, trained a custom reward model on human feedback, and applied PPO policy optimisation to steer SQL generation, serving 10K\+ daily transactions on AWS with measurable intent alignment gains\. • Engineered a multi\-stage agentic inference system with quantized LLM serving \(4\-bit/8\-bit\) cutting latency by 60% at F1 > 0\.95; designed a RAG layer over Pinecone for sub\-200ms retrieval, wiring tool\-calling, schema grounding, and safety validation into a single orchestration loop\. Stack: PyTorch · HuggingFace · PPO · RLHF · Pinecone · AWS · SQL

Open Source Developer Advocate at Qdrant, Sep 2025 \- Present · 11 mos, India, Qdrant Star contributor focused on vector search, RAG architecture, and real\-world retrieval systems\. • Built SQLRouter — a modular agentic Text\-to\-SQL system with semantic query caching over Qdrant, short\-circuiting redundant LLM calls at scale across intent classification, schema retrieval, SQL generation, and safety validation\. • Built Contract Voice Agent — a full\-stack agentic RAG system for document risk analysis with clause\-level embeddings, hybrid retrieval with pre\-tagged risk signals, and a real\-time conversational interface over FastAPI \+ React \+ WebSocket\. Stack: Qdrant · RAG · LLMs · FastAPI · Vector Search · Embeddings Featured on : https://qdrant\.tech/stars

Machine Learning Engineer at Quoppo, Oct 2024 \- Apr 2025 · 7 mos, Pune District, Maharashtra, India, • Designed hybrid LSTM\-MeshNet architecture for 3D positioning from scratch; diagnosed and resolved vanishing gradient failure modes via custom multi\-term loss scheduling and adaptive LR warmup, achieving 30% accuracy improvement over SOTA baseline\. • Rebuilt GAN training loop with redesigned discriminator and curriculum\-based fine\-tuning; reduced post\-production editing time by 40% and established reproducible MLOps foundation \(W&B \+ DVC \+ CI/CD\) across a 3\-engineer team\. Stack: PyTorch · GANs · LSTM · Weights & Biases · DVC · GitHub Actions

Undergraduate Research Fellow at Centre for Visual Information Technology \(CVIT\), Mar 2024 \- Aug 2024 · 6 mos, Hyderabad, Telangana, India, • Implemented advanced neural network algorithms to enhance OCR accuracy by 20% on low\-resolution images\. • Collaborated with Meta Reality Labs for experimental validations, achieving a breakthrough in image processing technology\. • Conducted a comparative analysis between diffusion models and GANs to improve prediction accuracy by 15%\.

NLP Intern at Textify AI \(Backed by AngelList\), Oct 2023 \- Jan 2024 · 4 mos, Indore, Madhya Pradesh, India, • Developed and implemented a cutting\-edge ML\-based Ticket Qualification Platform using FastAPI, Langchain, Node\.js, and MongoDB\. • Achieved an impressive F1\-Score of 99\.8% for predicting ticket categories and 93\.7% for severity levels with the NLP model developed\. • Enhanced user experience by integrating FastAPI framework and Langchain algorithm to reduce translation errors and improve server response time\.

## Education

Bachelor of Engineering, Information Technology, Pune Institute of Computer Technology

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

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