AI Practice Lead

Engineering Foundation Non-negotiable. This is what we screen on first, and there is no AI in it. Data modelling across SQL and NoSQL, and a clear view of where each one breaks Event-driven architecture in production with Kafka, RabbitMQ or equivalent. Ordering, replay, idempotency, dead-letter handling Distributed systems failure modes: partial failure, retries, backpressure, timeouts, circuit breakers Caching strategy and invalidation, at a scale where getting it wrong hurt Observability. You have instrumented a system, not just read someone else's dashboard At least one cloud deeply. Not three superficially API and integration patterns against real enterprise surfaces such as ERPs, CRMs and data platforms CI/CD, containers, release engineering You have owned something in production. On-call, incidents, the whole thing Agent systems are distributed systems with a non-deterministic component in the middle. If the fundamentals are not there, the AI layer does not survive contact with production. What We Are Not Looking For Someone who lists LLMs on a resume but has only called the API in a notebook People who explain everything in terms of frameworks they have never deployed Consultants who can only narrate what others have built Engineers who cannot hold a client conversation, and client-facing people who cannot build How We Will Evaluate You Stage 0, System design with no AI. A distributed systems problem with no LLM in it. Event flow, data model, failure handling, scale. Twenty-five minutes. Stage 1, Technical screen. Walk us through a system you built. Bring an artifact: an eval set, a trace from a run that failed, a postmortem, a cost-per-task number. Something real, not slides. Stage 2, Architecture discussion. A business problem, live. Design the solution and defend the trade-offs, including the decision not to use agents. Stage 3, Client simulation. Present your approach to a non-technical executive audience, then advise them on how to build the capability internally.