MLOps Engineering Team Leader

Based in Dundee, Scotland, our R&D operation is a dynamic environment, where every developer can impact the flow of technology – from introducing the smallest library to making big infrastructure changes. We welcome open-minded developers who like to share knowledge and help each other to push Optimove forward using the cutting edge of today’s tech. The new MLOps team will be responsible for the seamless deployment, monitoring, and maintenance of machine learning models in production. Acting as the critical link between the data science and R&D teams, this team will ensure that ML models transition smoothly from development to production, maintaining high availability, scalability, and performance. Key responsibilities include: Managing and optimising existing ML model deployments to ensure reliability and efficiency. Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management. Collaborating closely with data scientists to understand and implement model requirements. Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems. Implementing robust CI/CD pipelines, monitoring systems, and infrastructure automation. Upholding best practices in security, cost management, and infrastructure design for cloud environments. This team will play a pivotal role in ensuring that ML initiatives drive value effectively while maintaining operational excellence and we're looking for a hands-on Engineering Leader to lead the MLOps team to success. Responsibilities: Lead and mentor the MLOps team Design, implement, and maintain scalable MLOps pipelines. Oversee deployment and monitoring of machine learning models in production. Collaborate with data scientists, product managers, R&D engineers and stakeholders to align on goals and technical strategies. Ensure high availability and performance of deployed ML systems. Define and enforce best practices for CI/CD, infrastructure automation, and model lifecycle management. Manage cloud resources and optimise costs (AWS preferred). Requirements : Proven experience leading MLOps or software engineering teams. Expertise in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn. Deep understanding of CI/CD tools (e.g., AWS CodePipeline, Jenkins, GitLab CI) and infrastructure-as-code (e.g., AWS CloudFormation, Terraform, etc.). Hands-on experience with AWS services such as S3, Lambda, SageMaker, ECS, and CloudWatch. Strong understanding of containerisation (Docker, Kubernetes) and orchestration. Experience with monitoring tools (e.g., Prometheus, Grafana). Excellent problem-solving and leadership skills. Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience).

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