Financiële diensten
Senior MLOps Engineer for end-to-end deployment of AI/LLM services in production
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Senior Machine Learning MLOps Engineer position within a large financial services organization. The role focuses on enabling production use of AI services in the Business Lending domain, working closely with data scientists, architects, and platform engineers.
You take ownership of the end-to-end deployment lifecycle for AI services, covering CI/CD pipelines, containerized deployments, observability, and environment management across DEV/UAT/PROD. You act as a technical bridge between data science teams and the platform organization to ensure GenAI and agentic workloads run reliably in Production. You also help design and maintain infrastructure for LLM-based and agentic AI applications, including container orchestration and API serving layers, and support integration with cloud-based AI services. The role includes proactive technical collaboration through design sessions with both the product team and the platform team.
Within a multidisciplinary team, you contribute to using agentic AI to simplify and automate manual work for customer-facing colleagues. You help ensure that operational platforms and pipelines are capable of supporting agents in production, while maintaining strong observability and reliable release practices for AI workloads.
Eisen
- Language: English and Dutch mandatory
- Experience with Generative AI and preferably Agentic AI
- Experience aligning with stakeholders and supporting platform teams
- Experience with orchestrating data pipelines and ML model pipelines
- Experience with ML Ops best practices and AI products productionalisation
- Python and Bash, and working experience with data science teams.
- Experience with relevant tooling: data engineering tooling
- Experience with relevant tooling: AI services
- Experience with relevant tooling: CI/CD pipelines for containerized deployments across dev/uat/prod
- Experience with relevant tooling: observability including tracing, structured logging, and monitoring for LLM-based workloads (latency, token usage, cost)
Wensen
- Experience with setting up a 'gold standard'/blueprint/way of working for ML models
- Experience with implementing feature stores
- Preferably experience within the banking sector
- Knowledge of Kubernetes/Docker
- Experience with LangGraph / LangChain