MLOps Engineer

Harnham; Amsterdam; Hybrid; EUR 70,000 - 110,000; Full time

Benefits offered by this job

Competitive salary

Hybrid in Amsterdam

Learning & development

Clear progression

Impactful ML projects

Job summary

Harnham is seeking a talented ML/MLOps engineer to help build and deploy machine learning systems at scale in a modern cloud environment. You will work closely with experienced engineers to productionise models and support real-time decision making.

Based in Amsterdam with a hybrid office model, you will contribute to training, monitoring, feature store development, and observability across ML pipelines, while collaborating with data scientists to improve performance and reliability.

Qualifications

• Strong Python skills with production-ready code.
• Understanding of ML concepts and exposure to ML models in practice.
• Familiarity with MLOps principles and model lifecycle management.
• Awareness of monitoring and system reliability in production environments.
• Exposure to cloud platforms such as AWS or similar.
• Experience with ML tools or frameworks such as scikit-learn, TensorFlow, PyTorch or Spark.
• Strong communication skills and a collaborative approach to problem solving.
• A growth mindset with curiosity and a willingness to learn.

Responsibilities

• Support the build and deployment of machine learning models in production.
• Assist in developing systems for training, monitoring, and maintaining ML pipelines.
• Contribute to feature store development across online and offline use cases.
• Work alongside data scientists to productionise models and improve performance.
• Help enhance monitoring and observability across ML systems.
• Take ownership of tasks and features with guidance from experienced engineers.

Skills

Python

ML concepts

MLOps

Monitoring

AWS

communication

Growth mindset

scikit-learn

TensorFlow

PyTorch

Spark

Tools

scikit-learn

TensorFlow

PyTorch

Spark

Job description

This is a high impact opportunity to build your career in MLOps within a production environment at scale. You will work closely with experienced engineers to develop and deploy machine learning systems that support real time decision making, while gaining hands on exposure to best practices in cloud, data, and model operations.

The Company

They are a global technology business focused on powering seamless digital experiences through advanced data and payment infrastructure. Operating at significant scale, they support high volume, real time transactions across international markets. Their engineering teams are collaborative and delivery focused, with strong investment in data and machine learning capabilities. This is a fast growing area of the business with excellent scope for development.

The Role

You will contribute to the development and maintenance of machine learning infrastructure within a modern cloud environment.

• Support the build and deployment of machine learning models in production
• Assist in developing systems for training, monitoring, and maintaining ML pipelines
• Contribute to feature store development across online and offline use cases
• Work alongside data scientists to productionise models and improve performance
• Help enhance monitoring and observability across ML systems
• Take ownership of tasks and features with guidance from experienced engineers

Your Skills and Experience

• Strong Python skills with the ability to write clean, production ready code
• Understanding of machine learning concepts and exposure to ML models in practice
• Familiarity with MLOps principles and model lifecycle management
• Awareness of monitoring and system reliability in production environments
• Exposure to cloud platforms such as AWS or similar
• Experience with ML tools or frameworks such as scikit learn, TensorFlow, PyTorch or Spark
• Strong communication skills and a collaborative approach to problem solving
• A growth mindset with curiosity and a willingness to learn

What They Offer

• Competitive salary and benefits package
• Hybrid working model with a collaborative office environment in Amsterdam
• Structured learning and development alongside experienced engineers
• Clear progression within a growing data and machine learning function
• Opportunity to work on large scale, real world ML systems with tangible impact