ML Ops Engineer

Help protect 9.5 million customers worldwide by ensuring machine learning models and pipelines remain reliable, scalable, and secure in production environments.

ML Ops Engineer

• Work arrangement: Hybride
• Location: Utrecht (NL)
• Working hours: 36 hours per week
• Employment: Fulltime
• Job function: IT

This is what we offer you

• Salary: Gross monthly salary between EUR 5,994 and EUR 8,563 (scale 10) for a 36-hour work week.

• Extras: a thirteenth month, 8% holiday allowance, and a 10% Employee Benefit Budget.

• Development budget: EUR 1,400 development budget per year for your growth and development.

• Hybrid working: a balance between home and office work (possible for most roles).

• Pension: decide for yourself the amount of your personal contribution.

You & Your Function

Build, operate, and continuously improve machine learning platforms that strengthen the bank's cyber defence. As an ML Ops Engineer, you ensure that detection and analytics models perform reliably in production, from deployment and monitoring to optimisation and lifecycle management. Working closely with security experts and data scientists, you turn advanced machine learning capabilities into stable, scalable solutions that help identify and respond to cyber threats.

Examples for Practices

• Build and optimise production-grade machine learning pipelines that support security analytics and threat detection.
• Improve model reliability and scalability through automation, monitoring, and cloud-native engineering practices.
• Enable security specialists to apply data science and machine learning techniques effectively in operational environments.

Facts & Figures

• Several machine learning models and pipelines are already running in production within the Security Operations Centre

Top 3 Responsibilities

• Build, maintain, and optimise production-ready machine learning pipelines that support advanced cyber security detection and analytics capabilities across the organisation.
• Implement monitoring, observability, and lifecycle management processes that ensure machine learning models remain reliable, performant, and compliant over time.
• Collaborate with data scientists and security professionals to operationalise new machine learning solutions and continuously improve existing platforms and tooling.

You will be part of the Security Operations Centre (SOC), where security experts and data scientists work together to strengthen the bank's cyber resilience through advanced analytics, threat detection, and machine learning.

Together we achieve more than alone

We are curious about what you add to our team of Security Operations Centre.

"Protecting the bank against cyber threats requires close collaboration between security specialists, engineers, and data scientists. By combining our expertise, we create solutions that are both innovative and operationally reliable."

The people of the Security Operations Centre work every day to proactively identify, analyse, and mitigate cyber threats that could impact the bank and its customers. Collaboration is our approach; as one technically skilled and results-oriented team within Rabobank. We continuously challenge ourselves to improve our capabilities and support each other's growth while delivering measurable impact.

Work on yourself and the world around you at the same time

For us, your development and that of society go hand in hand. That's why we want to invest in you and work together for a better world. We summarise this in one sentence: At Rabobank, you work on yourself and the world around you at the same time. You'll see this reflected in your personal development budget, our hybrid working environment, and a healthy balance between work and home. You can work on banking services for our private and business customers, as well as societal issues such as food and energy transitions.

You & Your Talent

• At least 7 years of experience in ML Ops, Machine Learning Engineering, or a similar role
• Proven experience deploying and operating machine learning models in large-scale enterprise production environments
• Strong experience with CI/CD, automation, cloud-native technologies, and infrastructure-as-code
• Self-sufficient and able to take ownership
• Pragmatic and results-oriented
• Strong collaborator who works effectively with multidisciplinary teams

A high-risk reliability check is part of the procedure.