MLOps Engineer

Key Responsibilities

• Deploy machine learning models into production environments using scalable and automated deployment practices.
• Build and maintain model serving infrastructure for real-time and batch inference use cases.
• Implement monitoring frameworks to track model performance, drift, latency, data quality and service reliability.
• Automate model retraining pipelines in collaboration with ML Engineers and Data Engineers.
• Manage model versioning, deployment lifecycle and rollback strategies.
• Operationalise CI/CD pipelines for machine learning workflows in collaboration with Platform Engineering teams.
• Ensure model deployments comply with security, governance, privacy and enterprise architecture standards.
• Support incident management, root cause analysis and resolution of model performance issues in production.
• Optimise model inference performance, scalability and cost efficiency across cloud environments.
• Collaborate with ML Engineers, Data Scientists, Big Data Engineers and Platform Engineers to ensure smooth transition from development to production.
• Maintain documentation for model deployment processes, monitoring dashboards and operational procedures.

Qualifications & Experience

• Bachelor's degree in Computer Science, Data Science, Information Technology, Engineering or a related field.
• 5–8 years' experience in Machine Learning Engineering, DevOps or MLOps roles.
• Strong experience deploying and maintaining machine learning models in production environments.
• Hands-on experience with model serving frameworks and MLOps tools (e.g. MLflow, Kubeflow, Sagemaker, Azure ML or similar).
• Experience with containerisation and orchestration technologies such as Docker and Kubernetes.
• Strong programming skills in Python and experience with REST APIs and microservices.
• Experience with cloud platforms such as Azure, AWS or Google Cloud Platform.
• Knowledge of model monitoring, drift detection and performance evaluation techniques.
• Experience with CI/CD pipelines for machine learning workloads is highly desirable.

Key Competencies

• MLOps and model lifecycle management
• Model deployment and serving
• Monitoring and observability
• CI/CD for ML systems
• Cloud computing and containerisation
• Python development
• API and microservices integration
• Model governance and version control
• Troubleshooting and incident resolution
• Collaboration in Agile delivery environments