Head of AI Engineering (f/m/x)

Your mission Own and evolve our AI engineering function — transforming a 15–20 person ML team from research-heavy to a high-throughput, production-grade organization. You’ll partner with the CTO on strategy, build the platform that unifies LLM access, RAG, and backend services, and ship reliable, scalable AI features that change how banks work.   Key responsibilities  Team leadership and org build Hire, mentor, and develop a high-performing team; set the technical bar, operating rhythms, and code/research review practices Organize sub-teams (e.g., Core Modeling, AI Platform/Infra, Integrations) with clear ownership, SLOs, and on-call Manage roadmap, capacity planning, and delivery across parallel initiatives Architecture and platform Own the LLM gateway: unified APIs and proxy layers for multi-provider routing (OpenAI, Gemini, Bedrock), with rate limits, fallbacks, and cost tracking Build high-performance RAG pipelines (ingestion, embeddings, vector stores, caching) with robust observability and safety guardrails Partner with Java/ NestJS teams to define clean async contracts, schemas, and eventing patterns; drive low-latency, scalable inference Model lifecycle and operations Lead end-to-end model and prompt lifecycle: data curation, training/fine-tuning, evaluation, deployment, rollback Establish LLMOps / MLOps : model/prompt registries, CI/CD, canary/A/B tests, offline/online evals, drift and cost monitoring Optimize inference throughput and cost (autoscaling, batching, quantization/distillation, caching) Strategy and collaboration Translate company goals into an AI/ML roadmap with measurable outcomes; balance exploration with reliability and cost Own build-vs-buy/vendor strategy for models, infrastructure, and data services; manage budgets and SLAs Governance and security Implement data privacy, security, and compliance practices (RBAC, secrets, auditability); track prompt/model lineage and reproducibility Define incident response, runbooks, and postmortems for AI features Your profile 5+ years as a backend engineer and 4+ years leading AI/ML engineering in production (10+ years total experience ideal) Deep architecture  expertise  in Java (JVM) and/or Node.js ( NestJS ), distributed systems, APIs, microservices, and messaging/streaming Hands-on with LLM stacks: orchestration (e.g., LangChain / LlamaIndex or custom), vector DBs (Pinecone, Qdrant , FAISS), cloud AI (e.g., AWS Bedrock) Proven operation of systems at scale (millions of daily API calls) with strong SLOs, observability, and incident management MLOps foundations: model registries, experiment tracking, CI/CD, Kubernetes, IaC (e.g., Terraform), security best practices Excellent communication and stakeholder management; strong product sense focused on shipping user-facing feature   Fluent German and English for daily team collaboration, stakeholder management, and technical documentation Nice to have  Experience with GPU/accelerator serving and optimization ( vLLM , TGI, Triton, ONNX Runtime) Cost optimization for LLM workloads (token budgets, dynamic routing, caching) Evaluation and safety/red-teaming for generative systems; startup/high-growth experience Impact metrics  Platform: adoption of a unified LLM gateway; standardized observability and cost reporting Delivery: 2–3 user-facing AI features shipped with clear SLOs and measurable impact Reliability/cost: reduced average latency and cost per request; autoscaling and caching in place Org: sub-team structure established ; improved code quality and on-time delivery; targeted hiring completed Our stack   Backend: Java (JVM), Node.js ( NestJS ); event-driven microservices; API gateways/proxies AI platform: Python, PyTorch , LLM orchestration, prompt pipelines/registry; vector DBs (Pinecone, Qdrant ); RAG services Infra/DevOps: AWS (incl. Bedrock), Kubernetes, Terraform, CI/CD, Observability ( OpenTelemetry , Prometheus/Grafana) Why us Because we value talent more than hierarchy. Because at neoshare, respon