We’re a Munich-based AI-first fintech scale-up (founded 2019) with offices in Munich, Frankfurt, and Sofia. Our SaaS platform brings banks, investors, and advisors together to collaborate on complex financial deals — making due diligence faster, smarter, and more transparent. Our AI features are already live with leading banks. Now we’re scaling.
The Role
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 Director of AI 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, andon-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/NestJSteams 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
- Optimizeinference 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