AI Solutions Architect

Role overview

This is a pre-sales technical leadership role focused on enterprise AI deployments, sitting at the intersection of sales conversations and deep engineering work. The position owns the technical evaluation cycle end to end, from discovery through architecture design, proof-of-concept delivery, and a clean handoff to the implementation team that builds it. It suits someone who can hold their own in a room of sceptical engineers while still translating business problems into infrastructure designs that survive production.

Responsibilities

- Lead technical discovery alongside account executives and provide honest assessments of fit. - Design target architectures covering deployment model, integration points, security posture, and data residency. - Scope and deliver proofs of concept with customer engineering teams, typically within a four to six week window. - Present and defend architecture decisions to technical stakeholders at every level, including sceptical reviewers. - Respond to security questionnaires, architecture reviews, and compliance documentation requests. - Hand off to implementation teams with documentation complete enough that nothing has to be renegotiated after signature. - Feed recurring technical objections and capability gaps back to product and engineering.

Requirements

- Engineering background with real production experience, not only pre-sales exposure. - Working knowledge of AI and machine learning deployment, including inference serving, model lifecycle management, and the practicalities of GPU workloads. - Strong grounding in cloud and container infrastructure. - Experience designing for enterprise concerns such as security, compliance, data residency, and integration with existing estates. - Hands-on ability to build a working proof of concept yourself rather than only specifying one. - Excellent written communication for architecture documents and formal responses. - Comfort presenting to mixed technical and business audiences and handling direct challenge in the room.

Nice to have

- Experience with retrieval-augmented generation architectures earned by shipping them rather than reading about them. - Familiarity with regulated-industry requirements in healthcare, financial services, or the public sector. - Exposure to on-premise or air-gapped deployment models. - Existing relationships within GPU vendor or hyperscaler partner ecosystems.

Benefits and work setup

- Remote-eligible, full-time position.