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
• Delivery and Architecture
• The work itself.
• Own end-to-end delivery of AI-native programs, from architecture through production
• Design multi-agent orchestration using LangGraph, CrewAI or equivalent. Agent topology, tool routing, memory, state, fallback and recovery paths
• Integrate agent systems with enterprise systems of record, not toy datasets
• Build RAG where RAG is the right answer, from chunking through retrieval, re-ranking and evaluation
• Run agentic coding workflows with Claude Code, Cursor, Codex or equivalent, and lead projects where AI writes significant portions of the codebase while you guide, review and ship it
• Work with MCP and shared-context tooling. Design what agents are permitted to reach and under what controls
• Bring governance into the design from day one: agent identity, privilege scoping, audit, human-in-the-loop, kill switches
• Contribute to reusable frameworks and accelerators the wider practice can use

Team and Practice

• Build AI engineering capability across the delivery organisation through mentoring, standards and review quality
• Evaluate new models, frameworks and tooling before the hype catches up, and kill the ones that do not earn their place
• Contribute to internal knowledge bases and practice assets

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.

Not a theory round. Expect to walk through something you have actually built: the architecture decisions, what broke in production, what you would do differently. If you cannot do that with specifics, this role is not the right fit.

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
• Delivery and Architecture
• The work itself.
• Own end-to-end delivery of AI-native programs, from architecture through production
• Design multi-agent orchestration using LangGraph, CrewAI or equivalent. Agent topology, tool routing, memory, state, fallback and recovery paths
• Integrate agent systems with enterprise systems of record, not toy datasets
• Build RAG where RAG is the right answer, from chunking through retrieval, re-ranking and evaluation
• Run agentic coding workflows with Claude Code, Cursor, Codex or equivalent, and lead projects where AI writes significant portions of the codebase while you guide, review and ship it
• Work with MCP and shared-context tooling. Design what agents are permitted to reach and under what controls
• Bring governance into the design from day one: agent identity, privilege scoping, audit, human-in-the-loop, kill switches
• Contribute to reusable frameworks and accelerators the wider practice can use

Team and Practice

• Build AI engineering capability across the delivery organization through mentoring, standards and review quality
• Evaluate new models, frameworks and tooling before the hype catches up, and kill the ones that do not earn their place
• Contribute to internal knowledge bases and practice assets
• 8-12+ years in enterprise software engineering and architecture
• 5+ years in cloud-native architecture and large-scale distributed systems
• 2-4+ years in GenAI, Agentic AI, LLM, RAG, and AI engineering programs
• Proven experience leading multi-million-dollar transformation programs and customer engagements