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 Senior Software Engineer to be part of it! Responsibilities : Architect and develop robust pipelines for ML model training, testing, and deployment. Implement and maintain CI/CD workflows for ML projects. Monitor production ML systems for performance, errors, and drift. Automate infrastructure provisioning and deployment using IaC tools. Collaborate with team leader to define technical strategies. Requirements : 4+ years of experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. Knowledge of data engineering concepts (e.g., ETL, data pipelines). Ability to troubleshoot complex production systems. Strong communication and collaboration skills.
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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 Senior Software Engineer to be part of it! Responsibilities : Architect and develop robust pipelines for ML model training, testing, and deployment. Implement and maintain CI/CD workflows for ML projects. Monitor production ML systems for performance, errors, and drift. Automate infrastructure provisioning and deployment using IaC tools. Collaborate with team leader to define technical strategies. Requirements : 4+ years of experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. Knowledge of data engineering concepts (e.g., ETL, data pipelines). Ability to troubleshoot complex production systems. Strong communication and collaboration skills.
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