AI Engineering Manager

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.

Pay: Up to £80,000.00 per year

Benefits:

• Paid volunteer time
• Private medical insurance
• Work from home

Work Location: Hybrid remote in London