Who are we looking for?
We are hiring a technical owner for our in-house AI products. This is a mid-layer engineering role. You will own the products we have already built, upgrade them, take them through to deployment, and stay close enough to users that the product actually gets adopted.
You are a technical resource first. You write, review, and ship. You understand the stack, the models, the infra, and the business problem well enough to make the right build decisions yourself.
What you will own
In-house AI products: architecture, upgrades, quality, and release until deployment
Technical execution for AI development happening inside the company
Software and hardware infrastructure the products run on (hosting, servers, environments, usage, cost, reliability)
Product adoption from a technical side: rollout, stability, usability, and feedback into the next build
What will be your key responsibilities?
Build, upgrade, deploy
Own development and upgrades from design through testing to production
Work in the codebase: architecture, AI tooling, integrations, and release quality
Definition of done is deployment. Code that is written but not live is not done
Keep the product secure: fewer regressions, less leftover WIP, faster path from change to live
Technical ownership
Own the in-house AI product(s) as a builder: what we ship, how it is built, and whether it holds up in production
Turn business problems into technical solutions, not a list of tickets for someone else
Make build vs buy vs ignore calls on AI tools, models, and frameworks
Infrastructure
Stay hands-on with hosting, servers, environments, usage, and cost
Size infra to the product.



Reliability and spend are part of the job, not a side conversation
Keep product decisions and server reality aligned
AI and technology
Stay current on AI tools, models, frameworks, and how the market is moving
Recommend what we should adopt, what we should skip, and what we should build ourselves
Keep the stack current without chasing every new tool
Adoption and business lens
Treat adoption as a core outcome. A product that is live but unused has failed
Support internal rollout with a product that is stable, fast, and easy to use in real workflows
Measure usage and drop-off, then feed that back into what you build next
Entrepreneurial mindset: ownership, speed, trade-offs, and whether this makes the business better
What are the key requirements for the role?
4-6yrs of strong technical base: architecture, APIs, data flow, models, and deployment. You can build, not only describe
Hands-on with AI products and the current AI toolchain (LLMs, embeddings, agents, evaluation, orchestration)
Working knowledge of software and hardware/infra: hosting, servers, environments, basic networking and usage
Experience taking a product from build to live yourself, ideally an internal tool or AI product in a services / professional-firm environment
Cloud hosting, cost optimisation, and production monitoring experience
Product sense with a business lens: adoption, value, and cost, not only shipping features
Entrepreneurial mindset: ownership, bias to action, comfort with incomplete information
Clear communicator with engineers and with the business
Comfort helping people move from old workflows onto a new product
Mid-level: you can run this without being managed day to day

📌 AI Product Engineer (Mumbai)
🏢 treelife
📍 Mumbai