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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** STAFIDE - **Location:** Amsterdam, Netherlands - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, BigQuery, Continuous Integration, Github, Machine Learning, Software Engineering, Data Processing, Google Cloud, Delivery Pipeline, Model Validation, Containerization, Infrastructure Automation Frameworks, Low Latency, Machine Learning Operations, Terraform, Docker - **Published:** September 11, 2026 - **Apply:** https://www.adzuna.nl/details/5878715332 ## About the Role * 6-8 years of overall professional experience in Machine Learning, Data Science, or a closely related engineering discipline. * Strong hands-on experience developing, implementing, and maintaining machine learning models in production environments. * Strong understanding of the complete ML lifecycle, including model development, retraining, deployment, monitoring, and optimization. * Strong MLOps experience with ownership of production machine learning workflows and infrastructure. * Hands-on experience with Google Cloud Platform (GCP). * Experience with BigQuery and the Vertex AI ecosystem. * Strong experience with Terraform and infrastructure-as-code practices. * Hands-on experience with Docker and containerized ML workloads. * Strong experience building and managing CI/CD pipelines using GitHub Actions. * Experience with ML architecture design, optimization, testing, and automation. * Understanding of production ML monitoring, model performance, reliability, and low-latency deployment requirements. * Strong understanding of scalable and maintainable machine learning engineering practices. ## Description * Develop, implement, and maintain machine learning models for pricing ancillary products such as seats, bags, extra legroom, and paid fare upgrades. * Design, research, and implement end-to-end machine learning pipelines covering model training, retraining, deployment, and monitoring. * Lead the MLOps aspects within the team, ensuring robust, scalable, and production-ready machine learning solutions. * Design and optimize ML architectures to support reliable and efficient model development and deployment. * Continuously monitor, maintain, and improve productionized machine learning models. * Ensure low-latency model deployments and adherence to internal engineering standards and best practices. * Work extensively within the Google Cloud Platform (GCP) ecosystem for machine learning development and deployment. * Leverage BigQuery and the Vertex AI suite for data processing, model development, deployment, and monitoring. * Implement infrastructure-as-code using Terraform to provision and manage ML infrastructure. * Containerize machine learning applications and services using Docker. * Build and maintain CI/CD pipelines using GitHub Actions. * Implement testing, automation, and deployment practices to ensure reliable and scalable ML solutions. * Collaborate with data science, engineering, and other technical stakeholders throughout the machine learning lifecycle., * Design and implement end-to-end production-grade machine learning pipelines. * Develop and maintain ML models that address real-world pricing and product optimization problems. * Manage the complete model lifecycle from development and retraining through deployment, monitoring, and continuous improvement. * Design scalable ML architectures and optimize them for performance, reliability, and low-latency execution. * Lead MLOps practices within a technical team and establish effective engineering standards. * Build and maintain reliable CI/CD pipelines for machine learning applications. * Automate infrastructure provisioning and management using Terraform. * Containerize and deploy ML workloads using Docker. * Work effectively with GCP, BigQuery, and Vertex AI for production machine learning solutions. * Implement appropriate testing, monitoring, and deployment practices for production ML systems. * Troubleshoot production ML and infrastructure issues and implement sustainable improvements. * Collaborate effectively with data scientists, engineers, and other stakeholders. * Apply software engineering and MLOps best practices to machine learning development. What we bring to the table: * The opportunity to work on production-grade machine learning and MLOps solutions. * Exposure to real-world ML applications involving pricing and optimization of ancillary products. * Opportunities to work extensively with GCP, BigQuery, and Vertex AI. * Hands-on exposure to modern MLOps technologies including Terraform, Docker, and GitHub Actions. * Opportunities to work across the complete machine learning lifecycle, from model development and retraining to deployment, monitoring, and optimization. * A collaborative engineering environment focused on scalable, reliable, and high-performance machine learning solutions. * Opportunities to contribute to ML architecture, automation, testing, CI/CD, and continuous improvement. ## Related Videos - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Making Data Warehouses fast. A developer's story.](https://www.wearedevelopers.com/videos/302-making-data-warehouses-fast-a-developer-s-story) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)