AES - DE - Generative AI Solution Architect

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.
AI Engineering
Two capabilities that do not come free with an engineering background. These are the ones we probe hardest.
Evaluation and Error Analysis
You have built an eval set for a non-deterministic system and used it to make a decision
You can decompose a failure: is this a model problem, a retrieval problem, a data problem or a product problem?
You debug agent behaviour systematically, with traces and evidence, not by gut feel
Context Engineering
Deliberate management of what enters the model on each turn, and why
Memory strategy: what persists across steps, across sessions, and what should not
Tool surface design: what the agent can reach, how it is scoped, what happens when a tool fails
Cost and latency as first-class design constraints, not something discovered in the invoice
Consulting and Client Leadership
Equal weight to the engineering. This is a client-facing role in a services business.
Carry a market point of view on models, tooling and delivery patterns, and translate it into what it means for a specific client, in their language
Run discovery workshops, solution reviews and delivery cadences with client teams
Advise clients on standing up their own AI capability: practice structure, operating model, skills, governance, build versus buy, and adoption across their engineering org
Shape and defend technical proposals, PoC plans and roadmaps. Own the story end to end
Translate business problems into architectures for CXO-level stakeholders without hiding behind jargon
Say no when the answer is no. Tell a client when agents are the wrong tool, and be able to explain why