Position
Senior Machine Learning Ops Engineer — 1.0 FTE
Role overview
As a Senior MLOps Engineer you will own the end-to-end deployment lifecycle for AI services: CI/CD pipelines, containerized deployments, observability and environment management (DEV/UAT/PROD). You act as the technical bridge between data scientists and platform teams, ensuring GenAI/agentic workloads run reliably in production. You will design and maintain infrastructure for LLM-based and agentic AI applications and integrate with Azure AI Services. Active participation in design sessions and proactive contribution of solutions is expected.
Key responsibilities
- Design, build and maintain CI/CD pipelines for ML services and containerized deployments.
- Manage environments across DEV, UAT and PROD.
- Implement observability for LLM-based workloads (tracing, structured logging, monitoring of latency, token usage and cost).
- Design and maintain API serving layers, container orchestration and integrations with Azure AI Services.
- Collaborate closely with data scientists, architects and engineers to productionalise AI products and agentic workloads.
Mandatory requirements
- Languages: English and Dutch (mandatory).
- Proven experience orchestrating data pipelines and ML model pipelines.
- Experience with MLOps best practices and productionalisation of AI products.
- Strong skills in Python and Bash; experience working with data science teams.
- Experience with Azure DevOps CI/CD pipelines for containerized deployments across DEV/UAT/PROD.
- Familiarity with Azure AI Services (e.g. Azure OpenAI Service, Azure Document Intelligence, Azure AI Search).
- Experience with data engineering tooling, preferably Azure Databricks.
- Experience implementing observability for LLM workloads (tracing, logging, monitoring token usage/cost).
Preferred / Nice to have
- Experience setting up a “gold standard” blueprint or way of working for ML models.
- Experience implementing feature stores.
- Experience in the banking sector.
- Knowledge of Kubernetes and Docker.
- Experience with LangGraph / LangChain.
- Familiarity with supporting services such as PostgreSQL and Redis (for state management and checkpointing).
Working context
You will work closely with data scientists, machine learning engineers and cloud engineers to deliver data-driven tools and services.
Application instructions
- Please submit a personal motivation showing the required competencies and a CV in English.
- In your CV, indicate whether an internal reference can be requested. If you are currently or recently worked at Rabobank, include the name and job title of the relevant team lead.
Eisen
- We are looking for 1 FTE
- Language: English and Dutch mandatory
- Suppliers can offer just one candidate for this position
- Please clearly indicate in the motivation field whether the candidate has any planned vacation or special leave during the duration of this assignment
- Please provide in the CV whether an internal reference can be requested
- If the candidate is currently working at, or has recently worked at Rabobank, we would appreciate receiving the name and job title of the relevant team lead
- Midlance constructions are not allowed according to Rabobank policy
- Suppliers must be aware of the laws and regulations regarding employment conditions and Rabobank’s Collective Labour Agreement
- This assignment is placed in scale 10
- We would like to receive the personal motivation of the candidate and CV in English
- Candidate should be submitted exclusively to Magnit – Rabobank during the exclusivity period of 4 business days on one request
- Furthermore, the candidate has to be available throughout the entire duration of the assignment
Wensen
- ["Python and Bash, and (preferably) working experience with data science teams","Data engineering tooling, preferably on Azure Databricks","Experience with setting up a "gold standard"/blueprint/way of working for ML models is a big plus","Experience with implementing feature stores is an advantage","Preferably experience within the banking sector","Knowledge of Kubernetes/Docker is an advantage","Experience with LangGraph / LangChain is an advantage"]
Hoe werkt het sollicitatieproces?
Solliciteer direct
Vul je gegevens in via het formulier hieronder. Duurt minder dan 2 minuten — geen account vereist.
Analyse binnen 1–2 werkdagen
Een van onze recruiters beoordeelt je geschiktheid kritisch en neemt persoonlijk contact op — via WhatsApp, telefonisch of e-mail, wat jij prefereert.
Aanbieding op maat
In samenspraak met jou maken wij de aanbieding in orde. Wij beheren het aanbiedingsproces en begeleiden je waar nodig.
Aan de slag
Wij blijven beschikbaar voor vragen en ondersteuning — ook na plaatsing. Transparant, persoonlijk en zonder verrassingen.
Veelgestelde vragen
Wat kost het als ik via Jobhob word geplaatst als ZZP'er?
Voor ZZP-opdrachten hanteert Jobhob een fee van 10% over het geboden uurtarief van de opdrachtgever. Dit wordt vooraf transparant gecommuniceerd — geen verborgen kosten of verrassingen.
Hoe snel hoor ik iets na mijn sollicitatie?
Binnen 1 tot 2 werkdagen neemt een Jobhob-recruiter persoonlijk contact met je op. We doen eerst een kritische analyse op geschiktheid en geven je altijd eerlijke feedback — ook als we je op dit moment niet verder kunnen helpen.
Via welk kanaal neemt Jobhob contact met mij op?
Alle correspondentie verloopt persoonlijk via het kanaal dat jij prefereert: WhatsApp, telefonisch of e-mail. Je geeft je voorkeur op bij je sollicitatie.
Kan ik solliciteren als ik niet zeker weet of ik geschikt ben?
Ja, zeker. Onze recruiter doet de kritische analyse op geschiktheid. Twijfel je? Solliciteer gewoon — wij laten je weten of er een match is en bespreken eventueel alternatieven met je.