AI Engineering Manager

AI Engineering Manager, London / Glasgow AI Platform Engineering \& Delivery * Support with the design and drive the delivery of the agentic AI strategy. * Lead the design and delivery of scalable AI systems across Azure, including multi-agent orchestration platforms and LLM-powered applications. * Own end-to-end engineering lifecycle: architecture, build, deployment, and optimisation of AI services. * Drive adoption of modern AI patterns including RAG, agent orchestration, and event-driven workflows. * Ensure production readiness through observability, resilience engineering, and cost optimisation. Agentic AI \& Orchestration * Oversee development of multi-agent systems using frameworks such as Semantic Kernel and AI Foundry. * Implement deterministic orchestration patterns, context management, and memory strategies. * Drive innovation in AI workflows including voice AI, real-time inference, and autonomous decisioning systems. * Ensure explainability and auditability across agent interactions. AI Security, Safety \& Governance * Embed secure-by-design principles across all AI workloads, including prompt injection defence and data protection. * Partner with AI Safety and Compliance teams to enforce standards aligned to OWASP GenAI, NIST AI RMF, and ISO/IEC 42001. * Implement guardrails for model usage, data handling, and fairness/bias mitigation. * Ensure full audit trails and traceability of AI decisions. Cloud \& Infrastructure Engineering * Lead engineering across Azure-native services including Azure OpenAI, AKS, API Management, CosmosDB, and Service Bus. * Ensure scalable, containerised deployments using Kubernetes with strong isolation and security practices. * Drive infrastructure-as-code adoption (Bicep/Terraform) and CI/CD automation pipelines. * Optimise performance, latency, and cost efficiency across AI workloads. Leadership \& Team Development * Build, lead, and scale high-performing AI engineering teams. * Provide technical mentorship, career development, and engineering standards. * Establish a strong engineering culture focused on quality, accountability, and continuous improvement. * Act as a senior escalation point for complex technical challenges. Stakeholder Engagement \& Strategy * Translate business problems into AI-driven solutions aligned to organisational strategy. * Collaborate with product, data, and leadership teams to prioritise and deliver high-impact initiatives. * Contribute to AI roadmap, investment planning, and capability maturity. * Communicate progress, risks, and outcomes to senior stakeholders. Skills / Experience Required: * 5+ years in senior engineering roles, with experience leading technical teams. * Strong hands-on experience with Azure AI ecosystem (Azure OpenAI, AI Foundry, Cognitive Services). * Proven expertise in building and scaling distributed, cloud-native systems (AKS, microservices, APIs). * Experience with LLM application design: RAG, prompt engineering, orchestration frameworks. * Proficiency in modern programming and automation (Python, PowerShell, REST APIs, IaC). * Understanding of data platforms (CosmosDB, SQL, Redis) and event-driven architectures. * Experience designing and deploying multi-agent or autonomous AI systems. * Familiarity with real-time AI (voice, streaming, event-based processing). * Understanding of AI evaluation, testing, and red-teaming methodologies. * Exposure to AI safety frameworks and governance models. * Demonstrated ability to deliver complex platforms from concept to production. * Experience operating in fast-paced, innovation-led environments. * Strong stakeholder management and communication skills Certifications (Desirable) * Microsoft Azure AI Engineer Associate * Azure Solutions Architect Expert * Relevant AI/ML or cloud certifications Mindset \& Leadership Style * Engineering-first leader: leads through hands-on capability and technical credibility. * Outcome-driven: focuses on delivering measurable business value from AI. * Pragmatic innovator: balances cutting-edge approaches with operational stability. * Security and ethics conscious: prioritises responsible AI at scale. * Collaborative and transparent: builds trust across technical and business teams. 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