Headline

AI Engineer | Agentic RAG & Multi-Agent Systems | LLM Evaluation & Production GenAI | Google Cloud, Vertex AI, Gemini | Ex-Quantiphi

About

I build AI systems that leave the demo and survive production. For the past 2 years, I've shipped Generative AI and agentic systems across healthcare, legal, and enterprise domains — primarily on Google Cloud, primarily solving the unglamorous problems that decide whether an LLM system actually works at scale: retrieval quality, agent reliability, and evaluation. What I've shipped: → A multilingual Agentic RAG pipeline for EU labour law (hybrid sparse + dense retrieval), hitting 89% response accuracy in a live legal chatbot → A multi-agent browser automation system (Google ADK + Gemini Computer Use) that cut manual workflow time by 75%, at 95% task execution accuracy → A document intelligence pipeline processing 16+ unstructured medical formats — including handwritten lab reports — that took extraction accuracy from 30% to 82.8%, unblocking a downstream clinical risk pipeline → A nutrition data extraction system (Gemini 1.5 Pro + RAG + XGBoost) validating 1,750+ menu items against a 400K-item database at 93.4% accuracy Beyond the build, I've been the technical point of contact for 10+ enterprise accounts across the US, EU, and South Africa during Gemini Enterprise onboarding — translating fuzzy business requirements into working systems, and explaining agentic architectures to stakeholders who don't care how the retrieval works, only that it does. I've also mentored 6 engineers on technical ramp-up and client communication. My edge: I sit at the intersection of technical depth and stakeholder fluency. I can debug a retrieval pipeline at 11pm and walk a VP through why it matters at 9am. Currently exploring AI Engineer / Applied AI Engineer roles where I can go deep on agentic systems, LLM evaluation, and production reliability — ideally at a team building AI-native products, not bolting AI onto existing ones. Google Professional Machine Learning Engineer | Google Associate Cloud Engineer Open to connecting with people working on agents, RAG, and applied AI in production.

Location

Bengaluru

AI stack

antigravity

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railway

Work experience

Machine Learning Engineer at Quantiphi, Aug 2024 - Jun 2026 · 1 yr 11 mos, On-site, Multilingual Agentic RAG Pipeline (Legal) Built a hybrid search RAG system (sparse + dense embeddings) powering a multilingual legal chatbot for EU labour law. Achieved 89% response accuracy in production. Multi-Agent Browser Automation Designed a multi-agent system using Google ADK and Gemini Computer Use to automate multi-step enterprise workflows end-to-end. Improved operational efficiency by 75% with 95% task execution accuracy. Medical Document Intelligence Pipeline Built a GenAI pipeline with Gemini 1.5 Pro to process 16+ unstructured medical document formats, including handwritten lab reports. Boosted extraction accuracy from 30% to 82.8%, enabling reliable downstream clinical risk stratification.

Machine Learning Intern at Quantiphi, Feb 2024 - Aug 2024 · 7 mos, Hybrid, AI-Driven Nutritional Analysis System Combined Gemini 1.5 Pro, RAG, and XGBoost to extract and validate nutritional data from 1,750+ menu items against a 400K-item food database. Reached 93.4% accuracy, replacing weeks of manual effort with an automated pipeline.

Student Member at Computer Society Of India CUSB, Sep 2022 - Feb 2024 · 1 yr 6 mos, Chandigarh, India

Team Lead at Events CSE III Yr Chandigarh University, Oct 2022 - May 2023 · 8 mos, Chandigarh, India

Education

Bachelor of Engineering - BE, Computer Science, CHANDIGARH UNIVERSITY