Applied AI Solution Architect
You will sit at the front of our AI engagements — the person customers turn to when they need an honest, technically credible answer to "should we actually build this, and how?" You will shape scope, set evaluation criteria, and own the architectural decisions that determine whether an engagement ships something useful or quietly stalls.
This is not a sales role and it is not a delivery role. It is the seat where customer judgment and technical depth meet. You will be in the room when CIOs and Heads of Data are deciding whether to trust us with their hardest problems, and you will be the person whose answers earn that trust.
Responsibilities
- Lead the discovery and shaping of new AI engagements: surface the real business problem, map data realities, and propose an architecture the engineering team can actually deliver
- Own the first 90 days of every engagement you scope — including evaluation strategy, success metrics, and the explicit list of things we will and will not commit to
- Sit alongside customer technology leaders (CIOs, Chief Data Officers, Heads of Engineering) as a peer. Walk them through tradeoffs in evals, latency, cost, data governance, and failure modes
- Translate customer signal into clear engineering direction. Stay close enough to delivery to course-correct early when the real-world problem looks different from the slide
- Contribute to our point of view: shape internal opinions on architectures, vendor choices, and the patterns we recommend (or refuse to recommend) to customers
- Mentor solution architects coming up behind you and help build the bench
Requirements
- Substantial hands-on experience designing and shipping production AI systems — not just advising on them. You can speak credibly about evals, RAG patterns, agent architectures, fine-tuning tradeoffs, and the operational realities of running models in production
- A track record of leading complex customer engagements end-to-end. You have written the SOW, defended the architecture, owned the difficult conversation when scope drifted, and earned referenceable outcomes
- Sharp opinions about what good looks like — and the judgement to know when to hold them and when to update them
- Strong written and verbal communication. You can write a one-page architecture brief that a CFO can act on, and you can defend a technical choice in a room of sceptical engineers
- Comfort with ambiguity. You are energised, not paralysed, by problems where the right answer isn't obvious yet
Nice to have
- Prior experience in a top consulting firm, AI lab, or systems integrator — particularly in a customer-facing principal role
- Domain depth in one of [financial services, healthcare, public sector, industrial, retail]
- Experience speaking at industry conferences or publishing technical writing on applied AI
