MLOps Engineer

Hybrid working – 3 days in London

Permanent

As anMLOps Engineer, you will play a key role in optimizing, automating, and managing the deployment of machine learning models in production environments. You will work closely with data scientists, ML engineers, and DevOps teams to streamline the machine learning lifecycle, from development to deployment, monitoring, and scaling in the cloud. Your expertise will help us deliver robust, scalable, and efficient solutions for autonomous systems.

Key Responsibilities:

• Design, implement, and maintain scalableMLOpsinfrastructure to support the development and deployment of machine learning models.
• Collaborate with data scientists and ML engineers to developCI/CD pipelinesfor seamless model training, testing, and deployment.
• Develop automation tools and scripts inPythonfor model training, tuning, and monitoring.
• Ensure smooth deployment of models onCloud Platforms(AWS, Azure, Google Cloud) and manage resources effectively.
• Optimize model serving performance and scalability in cloud environments.
• Implement model monitoring, logging, and alerting to ensure production systems are reliable and efficient.
• Support end-to-end model lifecycle management, from development to production.
• Apply best practices inCI/CD, infrastructure as code, and containerization (Docker, Kubernetes).
• Work closely with cross-functional teams to ensure smooth operations and scaling of ML models used inRoboticsandAutonomous Vehiclesapplications.

Requirements:

• 3+ yearsof experience in MLOps, DevOps, or a related field.
• Strong experience with Pythonand scripting for automation and integration with ML pipelines.
• Expertise in deploying and managing machine learning models inTensorFlow.
• Hands-on experience with CI/CDtools (e.g., Jenkins, GitLab CI, CircleCI) to automate model deployment.
• Experience working with at least oneCloud Platform(AWS, Azure, GCP), with knowledge of cloud-based infrastructure management.
• Solid understanding of containerization technologies likeDockerand orchestration tools likeKubernetes.
• Prior experience working in theRoboticsorAutonomous Vehicleindustry ismandatory.
• Strong understanding of model serving frameworks, model versioning, and model performance monitoring in production.

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