---
title: "Pratiksha Kadam - AI Engineer in Pune Division | Tal"
description: "Meet Pratiksha Kadam, AI Engineer at Yarnit based in Pune Division. AI stack includes antigravity, claude code, and claude."
image: "https://tal.club/meet/pratiksha-kadam/share-image"
---

# Pratiksha Kadam on Tal

## Headline

AI Engineer at Yarnit

## About

I build AI systems that actually ship to production — not demos, not POCs, not "coming soon\." In 2 years at KPIT Technologies, I've architected and delivered Generative AI solutions for three of the world's most iconic automakers: Ford, Jaguar Land Rover, and Honda\. Every system I've built runs in production, serves real users, and solves hard engineering problems\. Here's what that looks like in practice: 🔹 I'm building AI Intel Query, an Agent\-to\-Agent \(A2A\) NLP platform that translates natural language into precise SQL queries across multi\-gigabyte network datasets\. Think: "Show me all failed routing events on Bus X last Tuesday" → instant, accurate results\. No SQL knowledge required\. 🔹 I engineered a data pipeline processing 1M\+ network packets with anomaly detection that flags intrusions and replay attacks in real time\. I also integrated an LLM\-powered RAG system enabling plain\-language queries over complex automotive network behavior\. 🔹 I architected a Multimodal RAG platform that ingests text, charts, tables, and equations from complex engineering documents, and a Video Intelligence system that cut manual documentation time by 90% and LLM token costs by 40%\. My technical skills: → Generative AI: LLMs, Multimodal RAG, Agentic AI, A2A, Knowledge Graphs, Prompt Engineering → Backend: Python, FastAPI, AsyncIO, WebSockets, PostgreSQL, Redis → Frontend: React 19, TypeScript, Zustand, ECharts → Data: Polars, DuckDB, Pandas, Apache Parquet, scikit\-learn What drives me isn't just the tech — it's watching an engineer at Ford type a question in plain English and get an answer in seconds that used to take hours of manual trace analysis\. That's the kind of impact I want to keep building\. I'm always interested in connecting with people working on: • Production AI/ML systems \(especially RAG and agents\) • High\-throughput data engineering challenges Reach out if you're building something ambitious with AI — or if you just want to geek out about Gen AI\. I'd love to chat\.

## Location

Pune Division

## AI stack

antigravity

claude code

claude

codex

cursor

deepseek

## Work experience

AI Engineer at Yarnit, Sep 2026 \- Present · 3 mos, Bengaluru, Karnataka, India

Generative AI Engineer at KPIT, Nov 2025 \- Aug 2026 · 10 mos, Pune District, Maharashtra, India, Built AI Intel Query an A2A natural language platform that lets engineers query multi\-gigabyte network datasets in plain English instead of writing SQL\. Powered by a multiRAG architecture with hybrid vector\-graph retrieval, enabling instant answers from datasets that previously required hours of manual trace analysis\. Engineered a high\-throughput data pipeline processing massive PCAP network captures \(1M\+ packets per trace\) using Polars and DuckDB's zero\-copy analytical engine enabling instant data retrieval from multi\-gigabyte datasets without exhausting server memory\. Developed a secure, multi\-tenant analytics dashboard \(React 19, TypeScript, Zustand\) with role\-based access control enabling stakeholders to see high\-level routing insights while testers dive into granular packet\-level analysis through interactive Sankey flows and heatmaps\.

Generative AI Engineer at KPIT, Nov 2024 \- Oct 2025 · 1 yr, Pune District, Maharashtra, India, Built Packet Predator, an automotive network intelligence platform for Honda that processes and analyzes CAN/CAN\-FD/Ethernet traffic at scale, with real\-time anomaly detection and natural language querying capabilities\. Built a real\-time signal processing pipeline handling 1M\+ automotive network packets \(CAN/CAN\-FD/Ethernet\) via async FastAPI with WebSocket streaming, Redis caching, and PostgreSQL delivering sub\-second analysis that previously required batch processing\. Deployed ML\-based anomaly detection to automatically flag network intrusions and replay attacks in automotive traffic then integrated an LLM\-powered RAG pipeline so Honda engineers could query network behavior in plain English instead of parsing raw packet logs\.

Generative AI Engineer at KPIT, Aug 2024 \- Oct 2024 · 3 mos, Pune District, Maharashtra, India, Built a Video Intelligence system for Jaguar Land Rover that combines multimodal LLM analysis with speech recognition to enable semantic video search and automated report generation dramatically reducing manual documentation effort\. Built a Hybrid Video RAG Engine combining multimodal LLM frame analysis with Whisper\-based speech recognition enabling JLR engineers to semantically search through hours of test footage at sub\-second latency instead of manually scrubbing video files Reduced manual documentation time by 90% through automated report generation and cut LLM token costs by 40% via dynamic context assembly and intelligent caching turning a tedious multi\-hour documentation process into a one\-click operation

## Education

B\.Tech, Electrical, Electronics and Communications Engineering, Pimpri Chinchwad Education Trust'S\. Pimpri Chinchwad College Of Engineering

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

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