Methods Analytics is looking for a MLOps Engineer who preferably has worked in the defence sector. Location: 4-5 days/week onsite at one of the following locations: Worcester/Great Malvern/Gloucester/Poole or London. What You'll Be Doing as an MLOps Engineer: Collaborate with Cross-Functional Teams: Work closely with data scientists, engineers, architects, and other stakeholders to align MLOps solutions with business objectives, explaining complex technical concepts in accessible language for non-technical audiences. Automate Workflows and Ensure Reproducibility: Write scripts to automate ML workflows and ensure reproducibility of machine learning experiments, enabling consistent and efficient results. Set Up ML Environments and Deployment Tools: Configure and maintain ML deployment environments using platforms and tools such as Kubernetes, Docker, and cloud platforms (e.g., AWS, Azure), ensuring scalability and reliability. Develop CI/CD Pipelines: Build and maintain CI/CD pipelines to streamline model deployment and ensure automated, secure, and reliable model lifecycles from development to production. Monitor and Maintain Deployed Models: Conduct regular performance reviews and data audits of deployed models, tracking model drift and identifying opportunities for optimisation to enhance performance and reliability. Security and Vulnerability Management: Participate in threat modelling to identify and assess potential security risks throughout the ML lifecycle. Implement and maintain vulnerability management practices to proactively address security risks, ensuring the integrity and resilience of deployed models and infrastructure. Troubleshoot and Resolve Issues: Proactively troubleshoot issues related to model performance, data pipelines, and infrastructure, identifying and resolving root causes to maintain stability. Champion Best Practices and Compliance: Ensure solutions follow best practices in security, scalability, and compliance, particularly aligning with Secure by Design and high-assurance software requirements. Identify and Implement Reusable Solutions: Focus on reusability to maximise development efficiencies, reducing costs across programmes by identifying commonalities and building scalable solutions. Collaborate on Data Architecture: Work with data architects to ensure the MLOps pipeline integrates seamlessly within the broader data architecture, aligning with governance and compliance standards. Requirements: You Will Demonstrate: Technical Proficiency in Python and ML Frameworks: Experience with Python and ML frameworks like TensorFlow, PyTorch, or Scikit-Learn. Containerisation and Orchestration: Hands-on experience with containerisation and orchestration tools, such as Docker and Kubernetes. CI/CD Expertise: Proven experience developing and managing CI/CD pipelines using tools like Jenkins, Git, and Terraform. Knowledge of Cloud and ML Infrastructure: Experience with cloud platforms (AWS, Azure, or GCP) 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. 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. Strong Troubleshooting Abilities: Proficient in diagnosing and resolving model and infrastructure-related issues. 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. Scalability and Optimisation in Production Environments: Experience managing high-performance, low-latency data systems. Understanding of Agile Development Methodologies: Familiarity with iterative and agile development methodologies such as SCRUM. Familiarity with Recent Innovations: Knowledge of recent innovations such as GenAI, RAG, and Microsoft Copilot. 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. Working at Methods Analytics: Methods Analytics (MA) exists to improve society by helping people make better decisions with data. We value discussion and debate as part of our approach. We treat data with respect and use it only for the right purpose. By joining us you can expect: Autonomy to develop and grow your skills and experience. Be part of exciting project work that is making a difference in society. A supportive and collaborative environment. As well as this, we offer: Development: access to LinkedIn Learning, a management development programme and training. Wellness: 24/7 Confidential employee assistance programme. Time off: 25 days of annual leave a year, plus bank holidays, with the option to buy 5 extra days each year. Pension: Salary Exchange Scheme with 4% employer contribution and 5% employee contribution. Discretionary Company Bonus: based on company and individual performance. Life Assurance: of 4 times base salary. Private Medical Insurance: which is non-contributory. Worldwide Travel Insurance: which is non-contributory. Seniority level: Mid-Senior level Employment type: Full-time Job function: Information Technology J-18808-Ljbffr