Head of AI Engineering (f/m/x)

Join neoshare as the Head of AI Engineering in Frankfurt, leading AI initiatives and building a high-performing team in a fast-growing fintech environment.

• 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.
• 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.

• Team leadership and org buildHire, 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, andon-call
• Manage roadmap, capacity planning, and delivery across parallel initiatives
• Architecture and platformOwn 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/NestJSteams to define clean async contracts, schemas, and eventing patterns; drive low-latency, scalable inference
• Model lifecycle and operationsLead 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
• Optimizeinference throughput and cost (autoscaling, batching, quantization/distillation, caching)
• Strategy and collaborationTranslate 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 securityImplement data privacy, security, and compliance practices (RBAC, secrets, auditability); track prompt/model lineage and reproducibility
• Define incident response, runbooks, and postmortems for AI features

• 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/LlamaIndexor 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
• MLOpsfoundations: 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
• 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
• 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 structureestablished; improved code quality and on-time delivery; targeted hiring completed
• 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)