Software Engineer

MLOps Engineer/Software Engineer (Machine Learning)

Location: Hybrid

Start: ASAP

Duration: 12 months renewable

Enable fast, secure and scalable machine learning in production

We’re looking for a seasoned MLOps Engineer to support a range of AI deployments across financial services and cloud-native SaaS platforms. You’ll join a cross-functional team building CI/CD pipelines and model management infrastructure that accelerates AI delivery.

You’ll be focused on infrastructure automation, pipeline governance and performance monitoring – everything that happens after the model is trained.

Key Responsibilities:

• Build ML pipelines and automate model versioning, deployment and rollback
• Support inference optimisation and system observability
• Implement best practices for data validation, testing and model governance
• Use tools like MLflow, Kubeflow, SageMaker, and Docker/Kubernetes

Key Skills & Competencies:

• Strong Python skills and experience with workflow orchestration (Airflow, Prefect)
• Familiarity with MLOps stacks, containerisation, and IaC (Terraform, Helm)
• Comfortable working in cross-functional squads with DevOps, Data and Security
• Bias toward automation, documentation and continuous improvement

I

deal for engineers who want to move fast without breaking things – this is the invisible engine behind high-performing AI teams.