I'm an AI research engineer at AutonomyAI, where I build AI coding agents that ship features into large existing codebases. Before that I spent two years at ANZ building LLM and RAG systems for the bank, and worked on deep learning for medical imaging at IIT Bombay.
I write about AI agents: what works and what breaks.
Writing
- Nov 26, 2025 · leaddev.comWhy AI Economics Are Fundamentally Broken
AI's variable costs never decrease with scale, which breaks the classic software growth playbook.
- Aug 13, 2025 · dataiku.comThe Agentic AI Cost Iceberg
Visible API costs are only the tip of what agentic AI actually costs in production.
- Jul 19, 2025Why I'm Betting Against AI Agents in 2025 (Despite Building Them)
I've built 12+ AI agent systems across development, DevOps, and data operations. Here's why the current hype around autonomous agents is mathematically impossible and what actually works in production.
- Dec 28, 2024I Tested AI Coding Tools So You Don't Have To: Here's What Actually Works
An honest review of AI-powered development tools including GitHub Copilot, GPT-engineer, Cody AI, and more. Real experiences, practical insights, and which tools actually boost productivity.
Projects
Genbase, an open-source platform for modular AI agent orchestration.
Vikray, a B2B agricultural marketplace connecting retailers and distributors.
Stark, a microblogging platform with real-time messaging and cross-platform apps.
ScriptGPT, a CLI that turns natural-language specs into working TypeScript.
Experience
- AI Research Engineer, AutonomyAIOct 2025 - Present
- Building agent harnesses: orchestration loops, tool design, context management, guardrails
- Multi-agent orchestration: planner and subagent loops, task decomposition, parallel execution
- Context engineering for long-horizon tasks: compaction, memory, retrieval
- Codebase ingestion and retrieval so agents can work in large production codebases
- AI Engineer, ANZJun 2023 - Sep 2025
- Built a dozen production agent systems for engineering workflows across the bank
- Fine-tuned Gemini models on internal documentation for domain-specific answers
- Ran RAG systems in production across internal applications
- Brought LLM API costs down with caching and query optimization
- Won the Global Generative AI Hackathon
- ML Research Assistant, MeDAL Lab, IIT BombayMar 2022 - Jun 2023
- Trained nucleus segmentation models across datasets with incompatible label sets
- Published the work at BIOSTEC 2024
- Built deep learning models for automated histopathology classification
Research
Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification, published at BIOSTEC 2024 in Rome, with collaborators from IIT Bombay and Tata Memorial Centre.
Public histopathology datasets label cell nuclei with incompatible class sets, so models are usually trained on one dataset at a time. The paper proposes a class-hierarchy method for training a single model across them, improving both segmentation and classification.
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