Principal Engineer (Engineering Practice)

1) Drive AI adoption at scale Define and execute an AI adoption roadmap across the engineering organisation (e.g., coding assistance, test generation, incident support, documentation, knowledge search, SDLC automation). Identify high-value use cases, prioritise them with measurable outcomes, and take them from prototype to production. Establish repeatable patterns (playbooks, templates, golden paths) so teams can adopt AI consistently. Improve engineering productivity and quality through AI-enabled automation (CI/CD, code review, testing, release, observability). Work closely with Heads of Engineering, Product Owners, Architecture, and Operations to align priorities and remove blockers. Provide pragmatic guidance—balancing pace with safety, quality, and long-term maintainability. Design reference patterns for AI-enabled engineering workflows (e.g., RAG for internal knowledge, secure prompt patterns, evaluation harnesses, model routing, guardrails). Lead technical decisions on build vs buy, model selection, integration patterns, and platform capabilities. Ensure solutions align with enterprise architecture, cloud strategy, and engineering standards. Degree in Computer Science/Engineering or related discipline; MSc/PhD preferred. 10+ years' software engineering experience, ideally in financial services or other regulated environments. Proven track record improving engineering capability and delivery outcomes through modern engineering practices, standards and developer tooling. Practical experience applying AI/ML or GenAI in engineering contexts (e.g., copilots, RAG, code intelligence, test generation, incident triage). Experience designing secure, scalable platforms and APIs; familiarity with cloud-native patterns and CI/CD. Strong understanding of SDLC, DevSecOps, and production operations (observability, incident management, SRE practices). Excellent communication and coaching skills; able to lead through influence. Experience building internal developer platforms or “golden paths”. Familiarity with model evaluation, prompt engineering patterns, and guardrail techniques. Experience with regulated environments (financial services) and working with risk/compliance stakeholders. Contributions to internal standards, reusable libraries, open source, research, patents, or speaking at conferences.