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, 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.
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