Senior Machine Learning Ops Engineer 36u/w
Role details
Job location
Tech stack
Job description
That all Rabobank customers in the Business Lending domain use the service processes that you helped realize. Within one of the largest Tribes of Rabobank group, with no fewer than 650 colleagues, you will get the opportunity to shape how our data science squad will be using the model factory and other GDAP platforms by working both with data scientists, architects and engineers. Are you up for it?
Create impact
You are eager to help others be successful and always think a few steps ahead. You love to challenge the status quo and are eager to come up with and propose creative solutions to problems in a rapidly changing and complex environment. With your background, you can easily connect the dots and help to continuously improve our strategy.
As a senior MLOps engineer, you will be working with the Model Factory team (platform team) as well as the Data science team. You will own the end-to-end deployment lifecycle for AI services. From CI/CD pipelines and containerized deployments to observability and environment management (DEV/UAT/PROD). You act as the technical bridge between our data sccientist withtin the team and the platform team, ensuring our GenAI/Agentic workloads run reliably in Production. You will design and maintain parts of the infrastructure for LLM-based and agentic AI applications, including container orchestration, API serving layers and integration with Azure AI Services. Being proactive to brainstorm and having design sessions within the team and platform team.
With each other
Collaboration is at the heart of everything we do. With a team of 7 data scientists, machine learning engineers, and cloud engineers you shape the way we do business within the Tribe.
As a team, we leverage data through advanced analytics to deliver data-driven tools to our stakeholders in the domestic business lending domain. Our data science products facilitate evidence-based agile strategic and tactical decision-making for Tribe Business Lending. Except to support use cases on:
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Shaping the new Road Map for our Retention Strategy with AI algorithms and recommendations for retention campaigns.
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Helping our portfolio management team to make the best strategic decisions based on a portfolio simulation tool or forecasting future portfolio inflow.
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Helping on setting acceptance strategy for new applications based on a simulation tool powered by AI evolution strategy algorithm
With you
You will be responsible for designing, building, and interpreting algorithms that power data products at Rabobank.
Requirements
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Orchestrating data pipelines and ML model pipelines
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ML Ops best practices and AI products productionalisation
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Python and Bash, and (preferably) working experience with data science teams.
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Relevant tooling:
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Data engineering tooling, preferably on Azure Databricks
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AI Services such as Azure OpenAI Service, Azure Document Intelligence, Azure AI Search
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Azure DevOps CI/CD pipelines for containerized deployments across dev/uat/prod
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Observability: Tracing, structured logging, and monitoring for LLM-based workloads (latency, token usage, cost)
Nice to have:
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Experience with setting up a 'gold standard'/blueprint/way of working for ML models is a big plus
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Experience with implementing feature stores is an advantage
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Preferably experience within the banking sector
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Knowledge of Kubernetes/Docker is an advantage
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Experience with LangGraph / LangChain is and advantage