MLOps Engineer: Real-Time ML Infra in Cloud (Hybrid)

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

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.