MLOps Engineer

MLOps Engineer

~2 days a week in the London office (hybrid - there is some flexibility here)

About the Company

This is an opportunity to join a platform-based technology company that operates at scale and is investing heavily in AI infrastructure to support growth across its search and recommendation systems. The company’s technical teams are modernising the core of a global digital marketplace, with a focus on fast experimentation, resilient production systems, and seamless machine learning integration.

You’ll be part of a cross-functional environment that values engineering excellence, collaboration, and building for impact.

What You’ll Be Doing

• Design and maintain machine learning infrastructure that supports the entire lifecycle of model development, training, deployment, and monitoring
• Deploy production models across systems handling high-traffic, latency-sensitive applications, including search ranking and content recommendations.
• Build CI/CD pipelines tailored to ML workflows, ensuring reproducibility, version control, and safe release cycles
• Implement containerisation and orchestration strategies using tools like Docker and Kubernetes to manage scalable deployments across environments
• Set up observability and monitoring tools to track performance, detect drift, and trigger automated retraining where required
• Contribute to the development of feature stores, data versioning processes, and internal tooling that accelerate experimentation and delivery
• Optimise serving infrastructure to support low-latency inference at scale
• Support A/B testing and experimentation frameworks to measure model impact in production
• Work closely with ML Engineers and Software Engineers to integrate models into live products using Python, APIs, and search technologies

What They’re Looking For

• Experience building end-to-end machine learning pipelines in a production environment, ideally with at least 3 years in an MLOps or ML platform role
• Strong Python skills and confidence in applying software engineering best practices, such as testing, version control, and code review
• Hands-on experience with orchestration and deployment tools, including Docker, Kubernetes, and CI/CD platforms
• Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation and deployment on major cloud platforms (AWS, GCP, or Azure)
• Comfortable working with ML libraries (e.g. TensorFlow, PyTorch, scikit-learn) and serving frameworks
• Knowledge of data pipeline orchestration tools such as Airflow or similar
• Understanding of monitoring stacks and observability tools such as Prometheus, Grafana, or ELK
• Bonus: experience with feature stores, vector search, and data versioning systems
• Bonus: familiarity with large-scale online platforms or marketplaces with high throughput and performance demands

If this role interests you and you would like to find out more (or find out about other roles), please apply here or contact us via niall.wharton@Xcede.com (feel free to include a CV for review).