Lead AI/ML Engineer building agentic systems that ship.
I design and deliver multi-agent platforms, RAG pipelines, and LLM-driven automation — the kind that run in production, not just in demos. Currently leading AI engineering at Travancore Analytics.
I build AI systems that hold up under real use.
I'm an AI/ML engineer with over 5 years of experience designing intelligent systems — from multi-agent LLM platforms to computer vision pipelines. I currently lead AI engineering at Travancore Analytics, where I own architecture, model integration, and delivery across a portfolio of production AI products.
My focus is on systems that survive contact with real users: agentic pipelines that stay reliable at scale, RAG assistants that don't hallucinate their way out of trust, and automation that measurably cuts manual work rather than just looking impressive in a demo. Outside client work, I build and ship personal projects end-to-end — including an algorithmic trading system spanning quant research to live execution.
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Architecture & delivery ownership
I lead system design end-to-end — from architecture decisions to production deployment — not just model integration.
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Agentic & RAG systems
Multi-agent orchestration with LangChain/LangGraph, grounded in retrieval pipelines that stay accurate as knowledge bases grow.
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Mentorship & code quality
I review code and mentor engineers on LangChain/LangGraph patterns, keeping teams moving without sacrificing reliability.
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Full-stack when it matters
Comfortable owning a project from FastAPI backend to React frontend when a product needs one builder end-to-end.
Where I've built it
Current
- Lead architecture and delivery across 5+ production AI platforms, including a multi-agent analytics assistant, an NL-to-SQL agentic system, and a metadata-driven code generator for clinical trials.
- Architected and shipped an agentic SQL query platform achieving 95% production reliability, integrating RAG over a vector database for grounded insight generation.
- Built a metadata-to-code generation pipeline (SAS/R) that cut biostatisticians' manual coding effort by 80%.
- Lead code reviews and mentor engineers on LangChain/LangGraph integration; own architectural documentation and feature roadmaps.
- Built deep learning document readers (OpenCV + YOLO + Tesseract) and face recognition/liveness detection systems for secure web-based attendance.
- Led migration of a production data pipeline from MongoDB to PostgreSQL, improving scalability and analytics performance.
- Optimized ETL processes and SQL queries, reducing processing time by 10%, and shipped interactive dashboards (Tableau, Metabase, Superset) into a Flask web application.
- Processed and analyzed large datasets with Apache Spark/Hadoop; applied classical ML and deep learning (CNN, YOLO) to computer vision problems.
- Built face detection/recognition models with OpenCV and delivered exploratory data analysis and visualization work.
Systems I've designed and shipped
A mix of production platforms built at Travancore Analytics and personal systems built end-to-end, solo.
DataLens — conversational analytics assistant
A multi-agent platform that automates exploratory data analysis, visualization, and insight generation through natural language. Agents handle ingestion, processing, and visualization end-to-end, surfaced through a conversational interface and a live KPI dashboard.
Algorithmic trading system — research to live execution
An end-to-end trading system combining a LangGraph multi-agent pipeline (news research → strategy planning → risk validation) with a quant screening engine (market regime detection → tradeability screening → factor/setup ranking). Integrated with Zerodha Kite for live Indian market data and order flow.
Genius CodeGen — clinical code generation
A metadata-driven agentic system that transforms structured metadata (shell JSON, table definitions, variable specs) into production-quality SAS and R scripts for clinical TFL and CSR tables, using an example-driven prompt learning framework to enforce structure and consistency.
AskMe — NL-to-SQL agentic analytics
Natural language to SQL conversion with real-time insight retrieval, backed by RAG over a Chroma vector database for grounded, context-aware analysis of Superset dashboards.
WhatsApp RAG support bot
Retrieval-augmented WhatsApp assistant for 24/7 customer support, grounded in a document-powered, continuously expandable knowledge base.
AI report generator for coaching platforms
One-click, AI-generated assessment reports for coaching professionals, with editable output so coaches retain full control over accuracy and client-specific framing.
FinTrack — automated expense tracking
Parses UPI transaction emails directly from an inbox (IMAP) into structured, categorized spending data — no manual entry required.
Tools I build with
AI / ML / Generative AI
Cloud & Infra
Computer Vision
Data & Backend
Education & recognition
- B.Tech, Mechatronics Engineering
Nehru College of Engineering & Research Centre, 2016–2020
- 1st Prize, Robo War — NIT Calicut & ATDU Kerala, 2018
- 2nd Prize, Robo War — IIT Palakkad, 2019
- Industrial Automation — Bosch, 2019
- Cyberdome Volunteer — Kerala Police, 2018
Let's build something that ships.
Open to full-time Lead/Senior AI engineering roles, and select consulting work — agentic systems, RAG pipelines, and LLM-driven automation.