ML engineer in MLOps

We are looking for an experienced ML Engineer specialising in MLOps to design, implement and maintain scalable Machine Learning infrastructure and production workflows. The ideal candidate will have strong hands-on experience with AWS SageMaker, particularly SageMaker Pipelines, Feature Store and Model Monitor, combined with solid expertise in infrastructure automation and CI/CD practices. This role will also absorb the DevOps responsibilities within the project, requiring someone capable of managing both ML infrastructure and deployment automation.

• Design, implement and maintain end-to-end MLOps workflows.
• Build and manage automated Machine Learning pipelines using AWS SageMaker Pipelines.
• Implement and maintain SageMaker Feature Store.
• Configure and manage model monitoring using SageMaker Model Monitor.
• Develop and deploy APIs to expose Machine Learning models and services.
• Implement infrastructure-as-code solutions to automate infrastructure provisioning.
• Design and maintain CI/CD pipelines for ML model deployment and infrastructure management.
• Automate model training, testing, deployment and monitoring processes.
• Ensure scalability, reliability and performance of ML production environments.
• Collaborate with Data Scientists and Data Engineers to operationalise Machine Learning solutions.

• Strong professional experience as an ML Engineer, MLOps Engineer or similar.
• Hands-on experience with AWS SageMaker.
• Practical experience with SageMaker Pipelines, Feature Store and Model Monitor.
• Experience developing and deploying APIs for Machine Learning applications.
• Strong knowledge of Infrastructure as Code (IaC).
• Experience with CI/CD tools and automated deployment workflows.
• Strong Python programming skills.
• Experience with cloud infrastructure, preferably AWS.
• Understanding of Machine Learning lifecycle management and model deployment.
• Previous experience managing DevOps responsibilities within an ML environment.
• Будет плюсом: Experience with Terraform or similar Infrastructure as Code tools; Experience with containerisation technologies such as Docker; Knowledge of Kubernetes; Experience in the energy or utilities sector

• Hybrid working model: 3 days onsite and 2 days remote.
• Full-time allocation.
• Immediate availability preferred.