Engineering Manager AI

Overview 

As Engineering Manager (AI) within Advanced Analytics (DA3) in the Chief Data & AI Office at Allianz Partners, you will manage our AI engineering teams: engineers who build agent-based AI services and conversational/voice AI systems that integrate with our digital insurance products. The remit covers two delivery surfaces: agent-based AI services consumed by domain teams via API (and conforming to Core Backend client libraries / API conventions where applicable), and voice/conversational AI experiences for customer-facing and operational use cases. 

You will own people management, performance, hiring, resourcing, and delivery leadership. Technical decisions stay with the senior engineers and technical leads in each team. Your role is to build the people structure, capacity, and ways of working that let those engineers ship applied AI into production. You'll partner with peer Engineering Managers on cross-team dependencies, with the AI Governance Review forum on AI compliance and ethics review, and with the Head of AI Platform & Engineering (HoP&E) as CAB chair on AI service change windows. 

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 

• Leads through coaching, not directing: defers technical authority to senior engineers and technical leads. 

• Comfortable in agile, iterative delivery environments with a clear bias for unblocking the team over centralising decisions. 

• Pragmatic about applied AI: focused on shipping AI capabilities into production for measurable business outcomes, not on research for its own sake. 

• Clear communicator across global, cross-functional stakeholders; able to translate AI capability and reliability into business impact for non-technical audiences. 

• Pragmatic adopter of AI-assisted developer tools (e.g., GitHub Copilot, Claude Code) and supports the team in integrating them into daily delivery. 

Nice to Have 

• Experience leading distributed AI delivery teams. 

• Background in voice or conversational AI at production scale (IVR replacement, contact-centre automation, voice-first product experiences). 

• Experience operating an embedded-specialist model alongside a central capability team. 

• Experience with AI governance frameworks: model cards, bias audits, regulatory attestations. 

• Insurance or financial services domain familiarity (claims, policy, payments). 

• Experience in regulated industries (insurance, finance, healthcare) where compliance, audit, and access control are part of standard delivery practice.