Huxley
AI Engineering Manager (Multi Agent)
City of London
Posted about 13 hours ago
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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.
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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
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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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Skills
Azure AI
Multi-agent Orchestration
LLM Application Design
RAG
Semantic Kernel
Kubernetes
Python
Infrastructure as Code
AI Security
Distributed Systems
Prompt Engineering
Event-driven Architecture
Stakeholder Management
Technical Leadership
AI Governance
CI/CD