Head of AI Engineering (f/m/x)
neoshare · Frankfurt am Main
What this role requires
4 requirements, read out of the advert rather than guessed from the job title:
Also mentioned, not required: Python, PyTorch, Kubernetes, AWS, Terraform, OpenTelemetry, Prometheus, Grafana, LangChain, LlamaIndex, Pinecone, Qdrant, FAISS. Worth having, but their absence is not what gets a CV filtered out.
See how often each of these is required across open backend roles in Europe.
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Job description
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
Read the full description (37 more sections)Show less
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, responsibility isn't delegated - it's owned.
Because we use modern AI and technology as a lever for exceptional results.
Because we develop people who want to learn, grow, and deliver.
Because performance, quality, and impact belong together for us.
Because we are working together towards building a European tech champion.
What You Can Expect Performance-driven, above-average compensation that rewards outstanding commitment.
High-end offices designed to support collaboration, wellbeing, and peak performance - including great health and fitness benefits.
Legendary team events where we celebrate our wins together and strengthen team spirit.
State-of-the-art AI tools, first-class equipment, and an environment that fosters ownership and personal growth.
Concentration of top talent, fast decision-making, and the chance to make a real impact early on.
Candidates must have the right to work in the EU; visa sponsorship is not provided for this role.
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