Principal Artificial Intelligence; AI Platform Engineer​/Architect

Description

We are looking for an AI Platform Engineer to drive our vision of AI-augmented engineering across our enterprise of 500+ engineers. In this role, you will empower our highly skilled workforce to deliver high-quality value to our clients faster and with a better Developer Experience (Dev Ex) than ever before. We need someone who combines deep technical expertise with practical platform engineering to create the scalable infrastructure, processes, and tooling that enable our teams to integrate AI into their development workflows safely and seamlessly.

Acting as both architect and hands‑on builder, you will articulate our vision for AI‑accelerated development practices, design platforms that embed AI at every phase of the SDLC, and champion adoption across our organization. We are seeking a highly skilled engineer who has recently specialised into AI technologies and can confidently guide our teams on both architecture and implementation.

Work Location:

Reigate, United Kingdom (Hybrid working model)

• Define and evolve the vision for AI‑enabled SDLC practices, translating business and technical strategy into concrete platform capabilities and processes
• Design and implement scalable AI platform infrastructure (SDKs, frameworks, deployment pipelines) that enables rapid, safe integration of AI into all phases of development—from coding and testing to deployment and monitoring
• Build operational processes, templates, and playbooks that guide teams through AI implementation while maintaining consistency, quality, and auditability
• Partner with teams across the organization to ope rationalise AI tools for SDLC acceleration (e.g., copilot‑style code generation, test automation, documentation, performance analysis)
• Create and maintain self‑service tooling for model evaluation, prompt engineering, A/B testing, monitoring, and compliance validation
• Establish and evolve patterns for data pipeline management, RAG/retrieval design, model versioning, endpoint management, and agentic orchestration
• Develop comprehensive documentation, architectural guidance, runbooks, and examples that reduce onboarding time and accelerate team adoption
• Evangelise AI‑enabled practices through presentations, office hours, and direct team engagement—building credibility and driving adoption across the organisation
• Provide escalation pathways for architecture questions and unblock teams on complex integration challenges
• Implement monitoring, observability, and governance systems that provide transparency without creating bottlenecks
• Collaborate with security, compliance, and data teams to embed safety guardrails into platform capabilities
• Participate in incident response and continuously harden the platform based on production learnings

What you’ll bring

Core Competencies

• Extensive background in software or platform engineering across multiple SDLC phases (with a proven track record of leading large‑scale, complex initiatives), with demonstrated expertise in infrastructure, developer tools, API design, or platform product development
• Demonstrated, hands‑on applied experience with LLMs, agentic frameworks, and GenAI systems (with proven hands‑on experience)
• Proven ability to design systems that abstract complexity and enable teams to self‑serve at scale
• Strong software engineering fundamentals (system design, testing, observability, operational excellence, SDLC practices)
• Experience building or maintaining developer‑facing platforms, SDKs, or internal tools
• Comfortable articulating technical architecture, vision, and strategy to both technical and non‑technical audiences

AI/ML & Ecosystem Knowledge

• Hands‑on experience with LLMs, agentic frameworks, and GenAI tooling (models, APIs, orchestration platforms)
• Practical experience with RAG architectures, prompt engineering, fine‑tuning workflows, and multi‑agent systems
• Experience with SDLC acceleration using AI (e.g., copilot‑style tools, automated testing, code generation, documentation)
• Familiarity with model deployment, versioning, inference optimisation, and observability
• Deep understanding of the rapidly evolving AI model and tooling landscape
• Azure / Microsoft ecosystem…