MLOps Fullstack Engineer

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 Full-Stack Software Engineer to be part of it!

• Develop and maintain pipelines for model deployment and monitoring.
• Collaborate with data scientists to integrate ML models into production systems.
• Manage cloud infrastructure and resources for ML workflows.
• Assist in automating repetitive tasks and implementing best practices.

• 2+ years of experience in DevOps, MLOps, or a related role.
• Proficiency in Python and ML frameworks like TensorFlow or PyTorch.
• Familiarity with AWS services (e.g., EC2, S3, SageMaker).
• Knowledge of CI/CD tools and workflows.
• Basic understanding of containerization (Docker) and orchestration (Kubernetes).
• Strong debugging and troubleshooting skills.
• Willingness to learn and adapt to new technologies.