Join a team where start-up pace meets the scale of an international banking group.
In this role, you will deliver AI and machine-learning solutions end-to-end—from agentic AI systems (LLMs, RAG, MCP, agent frameworks) to traditional ML models—and bring them into production across our network banks.
This role demands a polyvalent engineer who moves fluidly between deep technical work and direct engagement with business stakeholders across risk, fraud, operations, and customer analytics. You will be the bridge between complex technology and concrete business value.
Your mission at RBI:
Own use cases end-to-end: problem framing, build, deploy, monitor, iterate. Design and deliver both agentic AI solutions (LLM orchestration, RAG, tool integrations) and traditional ML models. Establish MLOps/LLMOps practices: experiment tracking, CI/CD, monitoring, cost management. Run rigorous evaluations: backtesting, A/B testing, drift detection, red teaming. Work with business partners across the bank to define requirements, explain trade-offs, and support adoption.
Your core competencies:
Experience shipping ML/AI systems to production (typically gained over ~5+ years). Financial services experience is a plus. Proficiency in Python and SQL;
experience using Spark for data processing.
Hands-on experience with both GenAI/agentic AI (LangChain, LlamaIndex, or similar; RAG; prompt engineering) and classical ML. MLOps fundamentals: MLflow, Docker, CI/CD, monitoring. The ability and willingness to engage a wide range of non-technical stakeholders: translating business problems into technical approaches, aligning on requirements, and communicating decisions and trade-offs.
Fluent
English required; German is an advantage.
Nice to Have:
Responsible AI and regulatory awareness (EU AI Act, model risk). Software engineering foundations: APIs, micro