The Role
Develop and operate production machine learning models and end-to-end MLOps pipelines for airline ancillary pricing. Responsibilities include model training, retraining, deployment, monitoring, architecture optimization, low-latency serving, infrastructure automation with Terraform, containerization with Docker, and CI/CD using GitHub Actions. The role uses GCP, BigQuery, and Vertex AI while leading MLOps practices and collaborating with data science and engineering teams.
Summary Generated by Built In
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.
Recruiter: Aswin Dhanvandhar
Phone: +31 20 369 0609 ; Extn :141
LinkedIn:https://www.linkedin.com/in/aswin-dhanvandhar/
Skills Required
- 6-8 years of professional experience in machine learning, data science, or a closely related engineering discipline
- Production experience developing, implementing, and maintaining machine learning models
- Understanding of the complete machine learning lifecycle, including development, retraining, deployment, monitoring, and optimization
- Strong MLOps experience owning production machine learning workflows and infrastructure
- Hands-on experience with Google Cloud Platform
- Experience with BigQuery and Vertex AI
- Strong experience with Terraform and infrastructure-as-code practices
- Hands-on experience with Docker and containerized machine learning workloads
- Strong experience building and managing CI/CD pipelines using GitHub Actions
- Experience with machine learning architecture design, optimization, testing, and automation
- Understanding of production machine learning monitoring, model performance, reliability, and low-latency deployment
- Ability to design and implement end-to-end production-grade machine learning pipelines
- Ability to lead MLOps practices and establish engineering standards
- Ability to troubleshoot production machine learning and infrastructure issues
- Ability to collaborate with data scientists, engineers, and technical stakeholders
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The Company
What We Do
STAFIDE is a Netherlands-based niche technology talent consulting company operating across Europe. It helps organizations identify, recruit, and deploy technology professionals in areas including cybersecurity, cloud engineering, software development, data analytics, ERP, infrastructure, and digital transformation. Its services include recruitment, workforce engagement, secondment, onboarding support, workforce deployment, and workforce analytics that support technology hiring and expansion.
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