Principal Engineer (AI Platform) (CPT Hybrid)

SUMMARY:
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POSITION INFO:
ENVIRONMENT: JOIN the team of a leading UK-based Gaming, Leisure & Entertainment Group seeking to fill the hands-on technical role of a Principal Engineer (AI Platform). 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. You will build and own the engineering harnesses on which AI-assisted delivery depends: the agent scaffolding, context and retrieval layers, evaluation and guardrail frameworks, and the telemetry that proves whether the model is working. It carries no line management accountability. Its authority derives from technical credibility, demonstrated results and delegated technical standards ownership, exercised across four technology stacks and every delivery team on the site. Applicants must have a Masters\/Bachelor’s Degree in Computer Science or similar discipline with 10+ years relevant experience including practical, shipped experience applying AI or Machine Learning to engineering workflows — not evaluation or advisory work alone. DUTIES: AI engineering harnesses: Primary – ·        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. Measured by : harness availability and adoption, evaluation coverage of AI-assisted output, defect and rework rate on AI-assisted changes, time from harness request to production availability. AI adoption in delivery: Primary for technical enablement, shared for realisation - ·        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. Measured by: active adoption rate across squads, proportion of eligible workflows AI-assisted, measured throughput and quality change attributable to augmentation, benefit realised against the committed transformation case. Domain and technical depth: Primary – ·        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. Measured by: accuracy of domain context assets, quality of technical adjudication in design review, absence of AI-attributable domain or regulatory defects reaching production. Technical standards and architecture practice: Shared with Architecture – ·        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. Measured by: standards adoption, audit and assurance findings, security review outcomes on AI-assisted code paths. Engineering telemetry and measurement: Shared – ·        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. Measured by: telemetry coverage across squads and stacks, data quality, timeliness and usefulness of insight delivered to leadership. Technical evaluation and vendor assessment: Recommends – ·        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. Measured by: quality and defensibility of evaluations, accuracy of realised outcome against predicted benefit and cost. Technical leadership without authority: Primary – ·        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. Measured by: capability uplift in senior engineering population, quality of design decisions across squads, peer assessment. Prototyping and technical risk reduction: Primary – ·        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. Measured by: proportion of major initiatives entering delivery with proven technical approach, avoided rework, cycle time from question to evidenced answer. REQUIREMENTS: Qualifications – ·        Master’s or 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 — not evaluation or advisory work alone. ·        Evidence of technical leadership without line authority across multiple teams. ·        Delivery accountability in a regulated, consumer-facing, high-availability environment (advantageous). Technical and professional depth - Deep, current, hands-on expertise in modern Software Engineering, with genuine production depth in at least two of Java, .NET, React and Flutter and working command of the remainder — sufficient to set standards and adjudicate technical trade-offs across the estate without deferring to others. Demonstrated depth in cloud-native architecture (AWS), microservices, event-driven systems, API design and CI\/CD engineering. Substantive practical experience with the AI Engineering stack: LLM application patterns, agentic workflows and tool use, retrieval and context engineering, prompt and workflow design, model evaluation and benchmarking, and the failure modes and safety considerations specific to generative systems in production. Experience building Developer-facing platforms and internal tooling used by other Engineers. Domain and regulatory understanding - The ability to acquire and hold deep domain understanding rapidly. Existing knowledge of online gaming, betting, financial services or another regulated, high-transaction-volume consumer domain is strongly preferred, along with an understanding of the technical control environment that regulation imposes — auditability, data protection, provenance and evidential requirements. Influence and communication - Critical. Every outcome this role owns is delivered through Engineers and leaders it does not manage and defended to stakeholders who are not technical. Requires the ability to build credibility with sceptical Senior Engineers through demonstrated work, to explain technical trade-offs to commercial and group leadership without dilution, to hold a technical position under pressure, and to write clearly enough that decisions survive without the author in the room.