AI Architect/Engineer (CPT Hybrid)

ENVIRONMENT:

JOIN the team of an Online Gaming Group seeking to fill the hands-on technical role of an AI Architect/ Engineer. You will serve as one of the site’s most senior individual technical contributors including to its AI-augmented engineering model. The role exists because the transformation the site has committed to, rebuilding how software is specified, written, tested and released around AI assistance, requires deep hands-on engineering ownership that cannot be delivered from a line management position and cannot be bought in as advisory.

DUTIES:

AI Engineering Harnesses:

• Accountable for the design, build, operation and lifecycle of the internal harnesses that make AI-assisted Engineering safe and repeatable: agent and assistant scaffolding, repository and domain context layers, prompt and workflow libraries, evaluation harnesses, regression and guardrail suites, and the integration of these into the Developer toolchain and CI/CD pipeline.
• Decide on the technical design and implementation of these systems.

AI adoption in delivery:

• Accountable for ensuring AI augmentation actually lands in the delivery teams: reference implementations, integration patterns per stack, Developer enablement, and removal of the technical obstacles that stall adoption.
• Work through Heads of Engineering and Engineering Managers, who retain accountability for their teams’ delivery outcomes.

Domain and technical depth:

• Accountable for holding sufficient depth in business domains — player journey, account and wallet, bonusing and promotions, game integration, payments, responsible gambling controls, regulatory reporting — to judge where AI assistance is safe, where it is not, and what a correct output looks like.
• Own the domain context and knowledge artefacts on which AI-assisted work depends.

Technical standards and architecture practice:

• Accountable for Engineering standards governing AI-assisted development: code provenance and attribution, review obligations for generated code, licensing and IP hygiene, secure coding under assistance, and the definition of done for AI-assisted change.
• Set these standards within the group reference architecture and recommends changes to it where augmentation requires them.

Engineering telemetry and measurement:

• Accountable for the technical instrumentation that makes engineering performance observable, delivery, quality and flow telemetry, and the pipelines and analysis layers that turn it into decision-grade evidence for the Managing Director and group stakeholders.
• Determine the technical implementation; the measurement framework and its interpretation are agreed with the Managing Director and Heads of Engineering.

Technical evaluation and vendor assessment:

• Accountable for the technical evaluation of AI platforms, models, tooling and third-party services proposed for engineering use, including capability benchmarking against workloads, security and data-handling posture, cost model and exit risk.
• Recommend to the Managing Director, who holds selection and contracting authority.

Technical leadership without authority:

• Accountable for raising the technical ceiling of the engineering organisation: mentoring Senior and Lead Engineers, leading design and architecture review, setting the standard for engineering craft, and building the internal advocacy that carries the augmentation model past initial resistance.
• Hold no line authority over any Engineer.

Prototyping and technical risk reduction:

• Accountable for de-risking novel technical initiatives ahead of committed delivery through working prototypes and spikes, and for producing a clear, evidenced recommendation to proceed, adapt or stop.

REQUIREMENTS:

Qualifications –

• Bachelor’s Degree in Computer Science, Software Engineering, Engineering or a related field.

Experience/Skills -

• 10+ Years in Software Engineering, with a sustained record of hands-on delivery at senior or staff-plus level.
• Demonstrated ownership of platform, tooling or Developer-experience systems used across an engineering organisation of comparable scale.
• Practical, shipped experience applying AI or Machine Learning to engineering workflows
• Evidence of technical leadership without line authority across multiple teams.
• Delivery accountability in a regulated, consumer-facing, high-availability environment (advantageous).