Developer – Machine Learning / MLOps - 10969327

Developer – Machine Learning / MLOps

Requirement ID: 10969327
Role: Developer – Machine Learning / MLOps
Location: Amsterdam, Netherlands
Work Arrangement: To be confirmed
Experience: 6–8 years
Start Date: 01 October 2026
Duration: 6 months
Core Competencies: Data Science, Machine Learning

Job Description

We are looking for an experienced Machine Learning Developer / MLOps Engineer to join a team responsible for developing and operating machine learning solutions for ancillary product pricing, including seats, baggage, extra legroom, and paid fare upgrades.

The successful candidate will have strong experience across the end-to-end ML lifecycle, from research and model development through retraining, deployment, monitoring, and continuous optimization. A strong focus on MLOps, ML architecture, CI/CD, infrastructure automation, and production reliability is essential.

The role will primarily operate within the Google Cloud Platform (GCP) ecosystem, using technologies such as BigQuery and Vertex AI.

Key Responsibilities

• Develop, implement, and maintain machine learning models for pricing ancillary airline products such as:
  • Seats
  • Bags
  • Extra legroom
  • Paid fare upgrades
• Design and implement complete machine learning pipelines, covering model development, retraining, deployment, and monitoring.
• Research and evaluate approaches to improve model performance and business outcomes.
• Productionize ML models and continuously monitor their performance.
• Optimize models and deployment processes to ensure low-latency production performance.
• Ensure ML solutions comply with internal engineering standards and best practices.
• Take a leading role in the team's MLOps activities.
• Design and optimize ML architectures for scalable and reliable production environments.
• Build and maintain CI/CD pipelines for machine learning applications.
• Implement automated testing and quality controls for ML solutions.
• Work extensively with Google Cloud Platform (GCP).
• Use BigQuery for data processing and analytics related to ML solutions.
• Leverage the Vertex AI suite for machine learning development and deployment.
• Use Terraform to implement Infrastructure as Code.
• Containerize ML applications using Docker.
• Develop and maintain CI/CD workflows using GitHub Actions.
• Collaborate with data scientists, engineers, and other technical stakeholders to deliver robust ML solutions.

Essential Skills

• 6–8 years of relevant experience in Machine Learning, Data Science, ML Engineering, or a closely related field.
• Strong experience developing and deploying machine learning models in production.
• Strong understanding of the complete ML lifecycle.
• Hands-on experience with MLOps.
• Strong expertise in Terraform and Infrastructure as Code.
• Proven experience designing and managing CI/CD pipelines.
• Experience with ML architecture design and optimization.
• Strong experience with testing and quality assurance for ML solutions.
• Hands-on experience with Google Cloud Platform (GCP).
• Experience with BigQuery.
• Experience with Vertex AI or the broader Vertex AI suite.
• Experience with Docker and containerized applications.
• Experience with GitHub Actions.
• Experience monitoring and improving production ML models.
• Understanding of low-latency ML deployments.
• Ability to build scalable, reliable, and maintainable ML solutions.

Desirable Skills

• Experience with pricing or revenue optimization models.
• Experience working with airline, travel, e-commerce, or dynamic pricing use cases.
• Experience developing models for product recommendations, upselling, or ancillary revenue.
• Broader experience with cloud-native data and ML architectures.

MLOps Focus

A key requirement of this position is the ability to lead the MLOps aspect within the team. The candidate should be particularly strong in:

• Terraform / Infrastructure as Code
• CI/CD pipeline development
• Automated testing
• ML architecture design
• ML architecture optimization
• Model deployment
• Model monitoring
• Model retraining
• Production performance optimization
• Scalable and reliable ML infrastructure

Candidate Profile

The ideal candidate is a hands-on Machine Learning Engineer with strong MLOps expertise who can bridge the gap between data science and production engineering.

They should be comfortable taking ownership of the complete ML lifecycle and ensuring that models are not only accurate but also reliable, scalable, maintainable, testable, and performant in production.