Public summary
A Munich-based AI-first fintech scale-up seeks a Head of AI Engineering to lead and transform their AI/ML team towards a high-throughput production-grade organization. The role involves owning AI strategy, building unified AI platforms integrating multiple LLM providers, managing lifecycle operations, and delivering scalable AI features for the fintech sector. Candidates should have extensive backend and AI engineering leadership experience, deep knowledge of Java/Node.js ecosystems, LLM operations, MLOps, and distributed system architectures. The position offers flexible working conditions, international collaboration, and a modern office environment.
Responsibilities
Lead and build the AI engineering team including hiring and mentoring; organize sub-teams with clear ownership and service level objectives; manage roadmaps, capacity, and delivery. Architect and maintain unified LLM gateways with multi-provider routing, cost controls, and fallbacks. Develop high-performance RAG pipelines and ensure robust observability and security. Collaborate with backend teams to integrate asynchronous, scalable inference services. Oversee model lifecycle management from data curation to deployment and rollback. Establish MLOps capabilities including registries, CI/CD, experiments, and monitoring. Drive AI/ML strategy aligned with company goals balancing innovation and cost. Implement governance, privacy, security, and incident response for AI features.
Qualifications
Minimum 5 years backend engineering experience and 4 years leading AI/ML engineering teams in production; ideally 10+ years total relevant experience. Strong architecture expertise in Java (JVM) and/or Node.js (NestJS), distributed systems, APIs, microservices, and streaming. Hands-on experience with LLM orchestration (LangChain/LlamaIndex/custom), vector databases (Pinecone, Qdrant, FAISS), and cloud AI platforms (AWS Bedrock). Proven track record managing large-scale, low-latency systems with strong SLOs and observability. Solid foundations in MLOps including model registries, experiment tracking, CI/CD pipelines, Kubernetes, infrastructure as code, and security best practices. Excellent communication and stakeholder management skills; product-oriented mindset. Bonus: experience with GPU/accelerator serving, cost optimization for LLM workloads, safety/testing of generative AI, and startup experience.