Qentelli - Lead AI Engineer

Lead AI Engineer

Job Location : Hyderabad

Experience : 8 to 15 Years

Np : Immediate to 15 Days

Mode of Work : 5 Days office - Fulltime

Shift Timings : General shift

Role Summary :

The Lead AI Engineer carries two connected mandates, not one. Day to day, you are a hands-on engineering lead inside the core platform team building and extending the agentic AI platform over the next several years: new agents, deeper orchestration and RAG capability, and the reliability and performance work that only shows up once a platform is carrying real usage. And when the platform is ready to go live at a client, you are the engineer who takes it from designed and specified to running, verified, and in the client's hands - inside their cluster, their network and identity systems, their change-control process.

The two mandates reinforce each other. You build the platform, so you understand it at the level of implementation detail that a deployment under client pressure demands - not just the architecture on paper, but where it's brittle, where it's been tested, and where it hasn't. And you deploy it, so the failure modes you see in the field - the assumption that didn't hold in a client's cluster, the edge case a client's data surfaced that the test suite didn't - feed straight back into how you build the next version. This is a permanent seat on the engineering team, not a rotation that ends when a rollout does.

You are not the architecture authority (that is the Technical Architect's role) and you are not managing the engineering organisation (that is the EM's role). You work alongside both, and alongside the Infrastructure & Platform Architect, translating the platform's architecture into working code and a working deployment - and, over time, becoming one of the people whose judgment shapes how the platform evolves, because you are the one who has both built it and watched it run in the real world.

Experience :

- 10+ years of professional experience in software or ML engineering, with at least 3 years specifically leading the deployment or productionising of complex distributed or AI systems - not a role bounded to your own team's environment, but one where you've taken a system into environments you didn't design and made it work there.

- Demonstrable experience closing the specific gap between works in our environment and verified running in a client-controlled environment - the skill this role is built around, not a side effect of a design or operations role.

- Direct experience deploying Kubernetes-based platforms into enterprise or client settings, including working within client-imposed security review and change-control constraints rather than a permissive internal environment.

- Experience being the primary technical presence during a client rollout - able to represent the platform, defend design decisions you didn't personally make at a working level, and make sound judgment calls when the architect isn't reachable.

- Hands-on familiarity with an agentic AI / LLM platform stack - graph databases, vector stores, event streaming, workflow orchestration, GPU-served inference - at the level required to bring it up and troubleshoot it, even if you weren't the one who designed it.

- A track record of clean handoffs: the client's own team was confident operating the platform independently after you left, not still dependent on you weeks later.

Education :

- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical discipline - or equivalent demonstrated through the deployment track record above.

- Relevant certifications - CKA, CKAD, AWS Certified Solutions Architect, GCP Professional Cloud Architect, or equivalent - are a positive contextual signal, particularly where paired with evidence of real client deployments rather than lab environments.