AI Product Engineer

Keka Technologies Private Limited; Maharashtra; On-site; INR 2,800,000 - 4,200,000; Full time

Job summary

Keka Technologies Private Limited is seeking a technical owner for its in-house AI products. This mid-layer engineering role will own the products, upgrade them, shepherd deployment, and ensure adoption by users.

You are a hands-on technologist who writes, reviews, and ships. You understand AI stacks, models, infra, and business problems well enough to make build decisions autonomously.

Qualifications

• 4-6 yrs of strong technical base: architecture, APIs, data flow, models, and deployment.
• 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.

Responsibilities

• 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 stable: fewer regressions, less leftover WIP, faster path from change to live

Skills

Architecture

APIs

Data flow

Models

Deployment

AI tooling

LLMs

Embeddings

Orchestration

Cloud hosting

Cost optimisation

Monitoring

Product mindset

Entrepreneurial mindset

Communication

Job description

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?

• 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 stable: 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