AI Engineering Manager
Job type:Permanent
Town/City:Munich
Region:Bayern
Sector:Data & AI
Client Company Type:In-House
Job ref:8184
Post Date:February 24, 2026
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About the Role
AI Engineering Manager
Job description
Key accountabilities
Qualifications
Job description
- Build, lead, and develop a high-performing engineering team delivering AI services (e.g., AI Assistant backend), including hiring, mentoring, and performance management.
- Own the end-to-end, full stack technical architecture and platform governance for shared components across brands, ensuring scalability, reliability, and interoperability.
- Partner with product and engineering leaders to help prioritize cross-brand AI use cases, maintain a shared backlog, and align delivery timelines and dependencies.
- Deliver platform capabilities such as agent runtime and store, fine-tuning and evaluation pipelines, SDKs/APIs, and semantic translation adapters for cross-brand workflows.
- Establish and enforce engineering best practices for AI services, including code quality, documentation, automated testing, observability, CI/CD, versioning, and release management.
- Ensure security, privacy, and compliance across services, including model transparency and monitoring, data protection, and incident response processes.
- Define and track platform KPIs (availability, latency, cost, quality metrics), implement performance and quality evaluation, and ensure continuous platform upkeep.
- Operate a cross-brand contribution model: review, generalize, and merge brand contributions into the common core through rigorous technical reviews and quality gates.
- Align technical decisions with business outcomes; communicate impact, capacity needs, and investment trade-offs; support budgeting and resource allocation for shared services.
Key accountabilities
- Impact on Business: Predominantly works tactically – executes strategic initiatives
- Innovation and Change: Predominantly develops new processes or solutions
- Accountabilities include: create new shared AI services and reference patterns; introduce automated evaluation and safety practices; standardize a cross-brand contribution model
- Communication scope: Predominantly communicates with others within the organization (option a); main communication partners: brand product leaders (planning/roadmaps), brand engineering managers (architecture/integration), executive leadership (status/KPI/investments)
- Decision-making power: Predominantly makes decisions; decisions include: approve platform architectures/standards, gate deployment readiness, accept/reject brand contributions based on quality gates.
Qualifications
- Educational Background: Bachelor’s or Master’s in Computer Science/Software Engineering or related; equivalent practical experience in large-scale platform engineering; relevant certifications (cloud architect, security, MLOps) are a plus
- Professional Knowledge and Experiences: 8+ years engineering with 3+ years leading platform/shared services; proven delivery of AI/ML platforms in production; cloud/Kubernetes/service mesh/observability; AI platform components (orchestration, vector stores, RAG, fine-tuning, eval harnesses); strong architecture across backend/data/APIs; security/privacy/incident response; SLO-driven operations/on-call/postmortems; cross-brand platform and contribution model management
- Other Skills and Competencies: Leadership and coaching; systems architecture and governance; product-minded delivery; SRE, observability with SLOs/SLIs, and cost/performance stewardship; security/privacy and model governance; stakeholder communication and clear documentation; cross-brand collaboration; languages: fluent English, German is a plus