---
title: "Vardan Malik - AI Engineer in Delhi | Tal"
description: "Meet Vardan Malik, Product & AI | Ex-DRDO, RRCAT, Kelly Technologies & AdGrid | Bennett University ’26 | Product · Analytics · AI/ML · Strategy based in Delhi. AI stack includes claude code, codex, and langchain."
image: "https://tal.club/meet/vardan-malik/share-image"
---

# Vardan Malik on Tal

## Headline

Product & AI \| Ex\-DRDO, RRCAT, Kelly Technologies & AdGrid \| Bennett University ’26 \| Product · Analytics · AI/ML · Strategy

## About

AI Engineer building conversational AI systems\. I’m a B\.Tech CSE graduate from Bennett University, focused on Generative AI, LLM agents, RAG, NLP, and Voice AI\. I enjoy building practical AI workflows using Python, LangChain, Ollama, APIs, prompt engineering, and evaluation\-driven development\. My focus is on making conversational systems reliable, grounded, and useful for real\-world users\. Currently looking for AI Engineer, GenAI Engineer, Voice AI Engineer, and AI Product roles\. Core areas: Generative AI, LLM Agents, Conversational AI, RAG, NLP, Voice AI, Python, LangChain, FastAPI, Ollama, Prompt Engineering, LLM Evaluation\.

## Location

Delhi

## AI stack

claude code

codex

langchain

langgraph

Figma Make

Qwen

## Work experience

AI Engineer at Infinity Soft Systems, Sep 2026 \- Present · 2 mo, Noida

Artificial Intelligence Intern at ADGRID, May 2026 \- Aug 2026 · 3 mo, Gurgaon, Built "Chotu," an on\-device Hindi/Hinglish conversational AI assistant enabling merchants to create discount and deal offers in natural language, running fully locally on a 3B LLM \(Qwen 2\.5 via Ollama\) with \~1s response latency and no cloud dependency\. Designed a "code for facts, model for reasoning" architecture — deterministic NLU parsers, value validation, and a fail\-closed confirmation gate around the LLM — achieving 185/190 routing reliability and zero unsafe writes across multi\-run regression suites\. Selected the production model via head\-to\-head benchmarking \(3B vs\. 7B: 35/35 successful deal creations vs\. freeze; \~1s vs\. 10–17s latency\), shipping the smaller model against the "bigger is better" default\. Rejected two proposed architectures with measured evidence before building them: RAG \(41% accuracy, 75% hallucination rate vs\. curated retrieval\) and an LLM intent\-classification router \(0/20 recall\), replacing both with deterministic alternatives\. Reverse\-engineered a production REST API contract from Postman collections and internal docs, built a contract\-accurate mock server, and migrated agent tool schemas to the real contract, verified via automated round\-trip tests\. Built Hinglish NLU components \(deal\-type detection, discount\-trigger and duration parsing, input validation\) with rigorous evaluation practice — multi\-run verification, model pinning by digest, and recall reported on uncontaminated test sets \(78%\)\.

Product Management at Shaip, Feb 2026 \- Apr 2026 · 2 mo, Ahmedabad

Machine Learning Intern at Defence Research & Development Organisation \(DRDO\), Jun 2025 \- Jul 2025 · 2 mos, Designed and implemented a bitstream\-based image transmission system using Python to simulate serial communication between sender and receiver devices\. Converted image data into byte\-level streams and reconstructed images using NumPy, OpenCV, and pySerial, emulating embedded system communication protocols\. Established virtual serial ports using socat to replicate hardware\-level UART data transmission\. Performed byte\-wise data handling, metadata synchronization, and validation to ensure lossless image reconstruction\. Gained hands\-on exposure to low\-level data transmission concepts relevant to embedded systems, surveillance, and defence communication systems\.

Machine Learning Intern at Raja Ramanna Centre for Advanced Technology \(RRCAT\), Dec 2024 \- Jan 2025 · 1 mo, Indore, Madhya Pradesh, India, Built ML models to predict peak current in flash lamps from voltage inputs\. Implemented Random Forest, Gradient Boosting, Polynomial Regression, and Neural Networks\. Evaluated models to identify the most accurate predictor using industrial datasets\.

Product Management Intern at Kelly Technology, Jun 2024 \- Jul 2024 · 2 mos, Analyzed user behavior data using SQL and Metabase, improving user retention by 8%\. Designed the proprietary Kelly Score to quantify application engagement\. Conducted usability testing and feature audits, increasing time spent by 17% and feature adoption by 4%\. Built a structured FAQ and bug reporting workflow, reducing support tickets by 15%\.

## Education

Bachelor of Technology, Computer Engineering, Bennett University

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

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