Engineering Manager AI

As Engineering Manager (AI) in Advanced Analytics, you lead AI engineering teams delivering agent-based and voice AI services integrated with digital insurance products. You shape people structures, capacity, and ways of working to enable production-readyApplied AI across surfaces while collaborating with governance, platform, and delivery leaders. You balance roadmaps, drive compliance with AI ethics reviews, and ensure scalable, cross-functional outcomes. You will empower senior engineers and tech leads to ship impactful AI at scale.

• Hire, onboard, performance manage, and develop ML/AI engineers, computational linguists, and embedded AI specialists
• Balance capacity across agent and voice surfaces in response to demand and strategy
• Coordinate embedded-AI-specialist model integration with backend engineering leadership
• Mentor technical leads in prompt engineering, agent design, NLU/NLP, and voice flow design
• Collaborate with AI Governance Review for compliance, bias review, and attestations
• Manage AI service change windows with HOPE-CAB and feed calendars to Run & Change service layer
• Balance agent and voice roadmaps and investments across surfaces
• Coordinate with backend leadership on production consumption of AI services
• Coordinate with frontend leadership on AI-driven UX patterns
• Lead hiring rubrics, interviews, calibration, and onboarding for AI delivery roles

• 8+ years in engineering with at least 3 years in people-management leading applied AI, ML, or conversational AI delivery teams
• Experience managing 6–15 engineers across two or more teams or specialisations (e.g., ML engineering and conversational/voice AI)
• Strong technical literacy in AI stack to mentor but not override leads: LLM-based agent systems (prompt engineering, agent design, evaluation, retrieval augmentation); Voice/NLP platforms (NLU/NLP, speech pipeline, dialogue management); Production AI deployments on Kubernetes (API design, observability, latency, cost); Embedded-specialist models in product teams
• Experience running structured hiring at scale for AI roles with rubric-based interviews, calibration, onboarding
• Experience with formal performance management cycles, career frameworks, and compensation calibration
• Familiarity with AI governance and ethics review processes and regulatory requirements in regulated industries
• Demonstrated ability to coordinate across organizational boundaries (peer managers, governance, backend/frontend, and external vendors)

• Strategic people leadership
• Cross-functional collaboration
• Clear communication to non-technical stakeholders
• LLM-based agent systems: prompt engineering, agent design, evaluation, retrieval augmentation
• Voice/conversational AI: NLU/NLP, speech pipeline integration, dialogue management
• Kubernetes-based production deployment of AI services: API design, observability, latency and cost control