Engineering Manager AI

Key Responsibilities

• Manage and grow the AI teams: hiring, onboarding, performance management, career development, and 1:1s for ML/AI engineers, computational linguists, conversational AI engineers, and AI specialists embedded with delivery teams.
• Capacity-balance engineers across the agent and voice surfaces in response to demand from domain teams; coordinate with senior leadership on team sizing and skill mix.
• Coordinate the embedded-AI-specialist model with the backend Engineering Manager: agree how specialists join domain delivery, how their performance is reviewed, and how their work is balanced between embedded delivery and central capability building.
• Coach and develop the technical leads in both teams: support them as authorities on prompt engineering, agent design, NLU/NLP, and voice flow design while keeping standards consistent.
• Work with the AI Governance Review forum (the dedicated AI-ethics body in the hub operating model) on AI compliance reviews: model choices, data handling, bias review, and compliance attestations.
• Coordinate change windows for AI service rollouts through the HoP&E-chaired CAB; feed the change calendar to the Run & Change service layer.
• Help balance the agent and voice roadmaps: ensure investment is staged correctly across each surface and the hybrid use cases that span both.
• Coordinate with backend leadership on how AI services are consumed in production.
• Coordinate with frontend leadership on AI-driven UX patterns.
• Work with delivery leads on ceremonies, cross-team planning, and continuous improvement of delivery flow.
• Run hiring loops: design interview rubrics for ML/AI engineers, computational linguists, conversational AI engineers, and embedded AI specialists aligned with the team's applied-AI delivery model.

Required Experience and Skills

• 8+ years engineering experience with at least 3 years in a people-management role leading applied AI, ML, or conversational AI delivery teams.
• Track record managing 6 to 15 engineers across at least two distinct teams or specialisations (e.g., ML engineering and conversational/voice AI).
• Strong technical literacy in the AI stack, enough to mentor and challenge but not to override technical leads:
• LLM-based agent systems: prompt engineering, agent design patterns, evaluation, retrieval augmentation.
• Voice and conversational AI platforms: NLU/NLP system design, speech pipeline integration, dialogue management.
• Production deployment of AI services on Kubernetes: API design, observability, latency and cost control.
• Embedded-specialist delivery models: placing specialists into product teams without losing central capability ownership.
• Experience running structured hiring at scale for AI roles: rubric-based interviews, calibration, and onboarding programmes.
• Experience with formal performance management cycles, career frameworks, and compensation calibration.
• Comfortable working with formal AI governance and ethics review processes; familiarity with AI compliance requirements in regulated industries.
• Demonstrated ability to coordinate across organisational boundaries: peer Engineering Managers, governance, backend and frontend delivery teams, and external vendors.

Ways of Working

Nice to Have