I lead GenAI engineering at UsefulBI, building multi-agent systems, their evals and their memory.
I also build open-source tools to understand how agents work, write about evals, and once topped a CUDA kernel leaderboard.
Open to workSenior or Lead AI Engineer roles · Bangalore, remote or relocation
I’ve been in this field since the classical ML era, back when “AI” meant feature engineering, LayoutLM and hand-tuned pipelines. These days it means agents, and the evals and memory that make them worth trusting.
Before industry I did speech and vision research at IISc Bangalore. I like building small tools to find out how things actually work, and sharing them in the open.
Find me on LinkedIn
Some start with a problem at work.
Some with “how does that actually work?”
Manish Code is a Claude Code-style coding agent I built from scratch in plain Python. One readable agent loop drives file, shell, edit, planning and skill tools over any OpenAI-compatible model.
The interesting work is in keeping it safe: a permission engine that blocks destructive commands and bypass tricks, a kernel-level OS sandbox with no network, and automatic context compaction so long sessions don’t fall over. Every file is small enough to read.
What is an agent actually remembering? OpenMemoryUI turns agent memory into a glass box: session, episodic, semantic and contextual memory update in front of you as you chat.
The useful part is being able to inspect every step. Each turn runs through a six-stage pipeline, from receive to write, with retrieval scores, prompt inspection, provenance and edit history. It connects to live models and MCP servers, or runs a zero-setup demo.
One line about what you’re doing, and a door policy for every open tab. Tab Bouncer is a Chrome extension that shows the tabs you don’t need the way out.
Under the hood there’s no agent loop and no scraping. It makes a single call to Jev, TypeSafe AI’s decision model, with your task and each tab’s title and address, and gets back a score and a category for up to 120 tabs in about a second, for a hundredth of a cent.
The Model Context Protocol is easy to name and surprisingly hard to picture. OpenMCP UI is a visual playground: ask in plain English and watch the client pick a tool, prepare its arguments and hand the call to the server.
Every step of the exchange is traced, from the handshake and tools/list to the final tools/call. A no-code workshop generates your own MCP servers, and it talks to OpenAI, Anthropic, OpenRouter and Gemini.
Can you cite a moment in a video the way you’d cite a page? Video RAG indexes a YouTube video in Qdrant, then answers questions with the frames and timestamps it used as evidence.
The answer view shows the frames it retrieved next to the answer, so you can check its reasoning instead of taking it on trust. There’s a short Loom walkthrough, too.
Building AI that has to work on Monday morning.
As Lead AI Engineer, since August 2025, I lead enterprise GenAI platform work: multi-agent orchestration, no-code workflow builders, evaluation systems, quality intelligence and AWS/EKS production deployment.
As ML Engineer II, from January 2024 to July 2025, I built production ML and LLM systems for construction-product intelligence: datasheet retrieval, attribute extraction, annotation automation and multimodal RAG evaluation.
ML Scientist, Dec 2022 to Dec 2023. Document KV and table extraction with LayoutLM, BROS and YOLO in production, integrated into 10+ client APIs supporting $80K-$100K MRR. Built Chat-AI with LangChain and Pinecone, and cut annotation from a full day to about two hours with GPT-4 extraction.
Research Assistant, Speech and Vision, 2021 to 2022. OCR and speech-recognition pipelines at SpireLabs, Indian Institute of Science: Hindi text and audio datasets, PyTesseract and EasyOCR with word-level accuracy, and Librosa MelSpectrogram audio work.
B.Tech in Electronics and Communication, NMIT Bangalore, GPA 8.98. The full story is in my resume.
Download resumeThe stack behind the work.
A few things I’ve written about agents.
Why a 90% agent can still fail most five-run checks.
Free for everyone.
Side quests with a scoreboard.
GPU MODE runs a competition where you write and optimise CUDA kernels for AI workloads. I hadn’t touched GPU programming in about three years.
Instead of hand-tuning, I ran a tight research, benchmark and iterate loop: an autoresearch-style harness that kept testing changes for two hours on a $20 plan. It reached rank #1. Sometimes the best way to relearn something is to build the loop that learns with you.
Visit GPU MODEResearch, benchmark, iterate, repeat. The same idea behind my eval harnesses, pointed at a kernel instead of an agent.
Read the write-upI’m open to contract projects and full-time roles in applied AI: agentic systems, evals, RAG and document intelligence. A messy prototype, a pipeline that needs gating, or an idea that needs a first working version are all welcome starting points.
