Lead MLOps Engineer – AWS SageMaker

Our requirements

• 5+ years of professional experience in Machine Learning Engineering, MLOps, or a closely related role.
• Strong track record of deploying, operating, and maintaining production machine learning systems.
• Expert-level Python skills and strong knowledge of the Python data science ecosystem.
• Hands-on commercial experience with AWS SageMaker — this is a key requirement for the role.
• Strong understanding of MLOps principles and the end-to-end ML lifecycle.
• Practical experience with MLflow, including experiment tracking and model management.
• Hands-on experience with GitLab CI/CD and building automated ML/CI/CD pipelines.
• Experience designing and building scalable ML systems and data/ML pipelines in a major cloud environment, preferably AWS.
• Proven ability to design, document, and communicate complex ML and data architecture.
• Experience with at least one major deep learning framework, such as PyTorch or TensorFlow.
• Experience taking ML models from development/research through to production deployment.
• Ability to collaborate with Data Scientists and other stakeholders and translate business requirements into actionable technical solutions.
• Proven experience providing technical leadership, mentoring, and guidance to Data Scientists, Data Engineers, MLOps Engineers, or Software Engineers.
• Strong understanding of software engineering best practices, including testing, version control, code quality, and maintainability.
• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

Optional

• Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Experience with ML monitoring and observability tools such as Prometheus, Grafana, or Evidently AI.
• Experience with production recommender systems.
• Experience working with globally distributed ML platforms or systems.
• Strong understanding of model performance, reliability, scalability, and production monitoring.
• Excellent communication skills and the ability to explain complex technical concepts and architectural decisions to both technical and non-technical stakeholders.

Your responsibilities

• Lead the architectural evolution of a live, globally deployed recommender system.
• Define and drive the MLOps strategy, standards, and best practices across the ML lifecycle.
• Design, build, and optimize CI/CD pipelines using GitLab CI/CD.
• Build and improve ML workflows focused on automation, scalability, reliability, and reproducibility.
• Use MLflow for experiment tracking, model management, and reproducible ML workflows.
• Productionize machine learning models and deploy them to AWS SageMaker.
• Collaborate closely with Data Scientists to move models from research/prototyping into reliable production environments.
• Design and maintain scalable ML and data pipelines.
• Implement robust monitoring, observability, and operational processes for production ML systems.
• Act as a technical advisor and mentor for Data Scientists, Data Engineers, MLOps Engineers, and other technical team members.
• Establish and promote software engineering best practices, including clean code, testing, documentation, and maintainability.
• Work hands-on with the Python codebase, developing ML and data infrastructure as well as deployment solutions.
• Translate business and product requirements into scalable technical solutions.
• Evaluate and introduce new technologies that can improve the organization's ML capabilities.

About the project

This is how we organize our work

This is how we work on a project

• Continuous Deployment
• Continuous Integration

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