Machine Learning Engineer (ID: 4050)

Stafide; Amsterdam; On-site; EUR 90,000 - 140,000; Full time

Job summary

Stafide is seeking a Machine Learning Engineer – MLOps to design, implement, and maintain end-to-end ML pipelines in production. You will own MLOps practices, working with GCP, Vertex AI, and BigQuery to deliver scalable, low-latency ML solutions for pricing ancillary products.

You will lead the full ML lifecycle from development and retraining to deployment and monitoring, collaborating with data scientists and engineers to ensure robust, automated CI/CD pipelines using GitHub Actions and

Qualifications

• 6–8 years of professional experience in ML, Data Science, or related engineering.
• Strong hands-on experience developing, implementing, and maintaining ML models in production.
• Strong understanding of the ML lifecycle from development to deployment and optimization.
• Hands-on MLOps experience with ownership of production ML workflows and infrastructure.
• Experience with Google Cloud Platform (GCP).
• Experience with BigQuery and Vertex AI ecosystem.
• Strong Terraform and infrastructure-as-code practices.
• Hands-on Docker experience with containerized ML workloads.
• Experience building and managing CI/CD pipelines (GitHub Actions).
• Experience in ML architecture design, optimization, testing, and automation.
• Understanding production ML monitoring, performance, reliability, and low-latency requirements.
• Strong knowledge of scalable, maintainable ML engineering practices.

Responsibilities

• Design and implement end-to-end production-grade ML pipelines.
• Develop and maintain ML models addressing 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 engineering standards.
• Build and maintain CI/CD pipelines for ML 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 ML solutions.
• Implement testing, monitoring, and deployment practices for production ML systems.
• Troubleshoot production ML and infrastructure issues and implement improvements.
• Collaborate with data scientists, engineers, and stakeholders.
• Apply software engineering and MLOps best practices to ML development.

Skills

ML lifecycle mastery

MLOps ownership

GCP experience

BigQuery

Vertex AI

Terraform

Docker

GitHub Actions

CI/CD pipelines

Production ML monitoring

Tools

GCP

BigQuery

Vertex AI

Job description

As a Machine Learning Engineer – MLOps, you will:

• 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.

What You Bring to the Table:

• 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.

You should possess the ability to:

• 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.

Let’s Connect

Want to discuss this opportunity in more detail? Feel free to reach out.