About
Product strategy, engineering, and applied AI in one role.
I’m a product and technology leader based in Bengaluru. I work across problem discovery, architecture, and delivery, and I stay hands-on with the systems I’m responsible for.

What I do now
Since 2019 I’ve been with Otomeyt AI, an AI hiring-technology company, in product and engineering leadership roles. I currently lead product. My work runs end to end: requirement discovery, BRDs and PRDs, prioritisation, validating ideas as proofs of concept, architecture direction, release planning, and client-facing rollout.
Over that time I’ve worked on 7+ production AI systems and more than 15 proofs of concept across résumé intelligence, semantic matching, assessments, code evaluation, video analytics, and conversational interviews. Several of those proofs of concept became production features. I also helped build and scale the company’s India operation, partnering with leadership on product direction and execution priorities.
Most of my production experience is in hiring technology. The patterns behind it travel well beyond hiring: evaluating open-ended work, extracting structure from documents, running conversational systems, and keeping people in the loop where judgement matters.
How I got here
I started in 2012 as a technical trainer at NIIT, teaching freshers and working professionals, then spent several months as an independent developer and trainer building college-level software projects.
At ASIT, as Technical Manager, I helped start a software-development division, managed delivery for new software initiatives, and defined the design and functional scope for a job-portal product.
I then worked in Cameroon with Paness IIHT as a Big Data & AI Consultant, delivering chatbots, data-mining use cases, and analytics-led business support for clients, and helping translate business requirements into implementable technical solutions.
My career began in training, and training and mentoring teams have stayed part of nearly every role since.
How I work.
01
Workflow fit before model cleverness
An AI feature that impresses in a demo can still fail in daily use. I judge it by whether it makes someone’s work meaningfully easier without forcing them to change tools or double-check everything.
02
Prove it small, then productise
I validate ideas as proofs of concept before committing a roadmap to them, and carry the ones that hold up into production features.
03
Platform judgement
Enterprise delivery constantly asks whether to build a reusable capability or a targeted customisation. Making that call well matters as much as the technology.
Tools I work with
- Python
- LLMs
- RAG
- Agentic AI
- LangChain
- OpenAI APIs
- Hugging Face
- Embeddings
- Vector search
- NLP
- AWS Lambda
- ECS
- EC2
- Docker
- Microservices
- REST APIs
- PostgreSQL
- Power BI
Perspectives.
I write occasionally on LinkedIn about building AI products that people actually adopt.
Get in touch.
The best way to reach me is a message on LinkedIn.
Message me on LinkedIn