Senior Software Engineer 1 (MLOps)

Extensive commercial software experience, with a track record of delivering working, maintainable code in a team setting.

Solid Python skills, including core data and ML-adjacent libraries (pandas, numpy, scikit-learn) and good instincts around code structure, testing and packaging.

Experience with cloud infrastructure at a practical level: deploying services, managing storage, working with access controls.

Familiarity with Kubernetes and Helm.

AWS experience preferred, strong experience with another provider considered.

Experience with ML infrastructure or data engineering: training pipelines, model serving, experiment tracking, or data pipelines.

Comfortable with CI/CD pipelines, version control, and containerisation as everyday tools, not just concepts.

Able to engage with technically complex and ambiguous problems, ask good clarifying questions, research and develop new skills, and work iteratively toward solutions.

Good communication skills: can explain what they’ve built, justify their decisions, explain what trade-offs they made, and flag up what they’re unsure about.

Exposure to LLM serving or fine-tuning workflows, even at small scale or in personal projects.

Understanding of data governance or security requirements in regulated industries.

Interest in privacy-preserving techniques (differential privacy, secure computation).

Experience with agentic AI frameworks (LangGraph, AutoGen, CrewAI, or similar).

Familiarity with Kubeflow, KServe, or similar ML orchestration platforms.

Familiarity with GitOps / Infrastructure-as-code (e.g. Flux, Terraform).