MLOps Engineer

at  Methods Business and Digital Technology

Job Title: MLOps Engineer
Job Type: Permanent
Location: No permanent workplace
Salary range: £55,000 - £65,000 per annum
Methods Analytics exists to improve society by helping people make better decisions with data. Combining passionate people, sector-specific insight and technical excellence to provide our customers an end-to-end data service. We use a collaborative, creative and user centric approach data to do good and solve difficult problems. We ensure that our outputs are transparent, robust and transformative.
We value discussion and debate as part of our approach. We will question assumptions, ambition and process – but do so with respect and humility. We relish difficult problems, and overcome them with innovation, creativity and technical freedom to help us design optimum solutions. Ethics, privacy and quality are at the heart of our work and we will not sacrifice these for outcomes. We treat data with respect and use it only for the right purpose. Our people are positive, dedicated and relentless. Data is a vast topic, but we strive for interactions that are engaging, informative and fun in equal measure.
Methods Analytics was acquired by the Alten Group in early 2022.

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.
  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

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

• 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, 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.