MLOps Engineer

Methods Analytics (MA) is recruiting for a MLOps Engineer to join our team within the Defence Business Unit on a permanent basis.

Location: This role is hybrid, primarily remote, with flexibility required to travel to client sites and our offices in London, Sheffield, and Bristol.

Requirements:

You Will Demonstrate:

Technical Proficiency in Python and ML Frameworks: Experience with Python and ML frameworks like TensorFlow, PyTorch, or Scikit-Learn, enabling efficient deployment and management of ML models.

Containerisation and Orchestration: Hands-on experience with containerisation and orchestration tools, such as Docker and Kubernetes, to ensure reliable, scalable model deployments.

CI/CD Expertise: Proven experience developing and managing CI/CD pipelines using tools like Jenkins, Git, and Terraform, streamlining deployment and automating testing.

Knowledge of Cloud and ML Infrastructure: Experience with cloud platforms (AWS, Azure, or GCP), infrastructure-as-code (IaC) practices, and managing cloud-based ML workflows and resources at scale.

Experience with Threat Modelling and Vulnerability Management: Proven ability to conduct threat modelling exercises to identify security risks and implement vulnerability management practices to ensure robust and secure machine learning systems.

Experience in Security and Compliance: Demonstrated experience working within secure, high-assurance environments, ideally including defence or similarly regulated settings.

Cross-Functional Collaboration Skills: Ability to collaborate across teams to translate business requirements into technical specifications, maintaining clear and effective communication.

Strong Troubleshooting Abilities: Proficient in diagnosing and resolving model and infrastructure-related issues, identifying root causes, and implementing corrective actions.

You may also have some of the desirable skills and experience:

Experience with MLOps Tools and Version Control: Familiarity with tools such as MLflow, DVC, Seldon Core, Metaflow, and Airflow or Prefect, and version control practices for models and datasets to ensure reproducibility, traceability, and compliance across ML workflows.

Scalability and Optimisation in Production Environments: Experience managing high-performance, low-latency data systems and optimising ML model infrastructure to handle large-scale data in production.

Understanding of Agile Development Methodologies: Familiarity with iterative and agile development methodologies such as SCRUM, contributing to a flexible and responsive development environment.

Familiarity with Recent Innovations: Knowledge of recent innovations such as GenAI, RAG, and Microsoft Copilot, as well as certifications with leading cloud providers and in areas of data science, AI, and ML.

Please note:

This role will require you to have or be willing to go through Security Clearance. As part of the onboarding process candidates will be asked to complete a Baseline Personnel Security Standard; details of the evidence required to apply may be found on the government website Gov.UK. If you are unable to meet this and any associated criteria, then your employment may be delayed, or rejected. Details of this will be discussed with you at interview