• Implement and continuously improve machine learning and AI operations frameworks
• Design and deliver tools, framework components and engineering practices for production-ready ML and AI systems
• Coach and guide a small team of MLOps Engineers, Analytics Engineers and AI Engineers
• Collaborate with Data Science, Pricing, Data Engineering and Software Development teams
• Evolve Machine Learning and AI Engineering standards and frameworks
• Enhance data pipelines and engineering infrastructure for scaled ML and AI solutions
• Support data acquisition, transformation, model discovery and development
• Ensure model auditability, versioning and data security
• Design and implement cloud AI Ops using Azure
• Optimise deployment of ML and AI model scoring code in production services
• Use CI/CD pipelines and manage deployment and versioning of data science and AI models
• Develop automated monitoring for model execution, quality, degradation and operational performance
• Manage remediation of priority 2, 3 and 4 production issues
• Conduct model testing, validation and test automation
• Deploy ML and AI models as API endpoints for internal and partner systems
• Lead system design and architecture discussions and share knowledge
• Liaise with senior stakeholders to improve strategic business decisions and identify opportunities
• Comply with the Group Code of Conduct, Fitness and Propriety policies, company policies, values, guidelines and relevant regulations

Requirements

• Extensive experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer
• Experience building integrations between cloud-based systems using APIs
• Experience developing and maintaining ML and AI systems
• Experience with agile ways of working in a data science, machine learning and AI environment
• Experience designing or maintaining data software development lifecycles and continuous integration and deployment (CI/CD)
• Exposure to machine learning and AI methodology and best practices
• Coaching experience
• Bachelor's or master's degree and/or equivalent professional experience
• Deep experience and strong understanding of Microsoft Azure, including Azure ML, Azure Stream Analytics, Cognitive Services, Event Hubs, Synapse, and Data Factory
• Fluency in Python and modelling frameworks such as PyTorch and TensorFlow
• Skilled in applying MLOps frameworks within a production environment
• Excellent verbal and written communication skills
• Strong time management and organisational skills
• Ability to diagnose and troubleshoot problems quickly
• Excellent problem-solving and analytical skills
• Strong stakeholder management and line-management ability
• Ability to work independently and as part of a team

Core Competencies

Demonstrates expertise in implementing and improving machine learning and AI operations frameworks, with a strong focus on cloud AI Ops using Microsoft Azure. Proficient in developing production-ready ML and AI systems, ensuring model auditability, and optimizing deployment processes.

Highest-signal resume keywords

• Machine Learning Operations (MLOps)
• Microsoft Azure
• Python Programming
• CI/CD Pipelines
• Coaching and Team Leadership

Hard Skills

• Machine Learning
• AI Systems Development
• Data Pipeline Engineering
• Model Testing and Validation
• API Development
• Data Software Development Lifecycle
• Model Versioning
• Automated Monitoring
• Data Transformation
• Model Deployment

Soft Skills

• Excellent Communication Skills
• Strong Problem-Solving Skills
• Time Management
• Organizational Skills
• Stakeholder Management

Certifications & Qualifications

• Bachelor's Degree
• Master's Degree

Industry Keywords

• Machine Learning Methodology
• AI Best Practices
• Agile Development
• Data Engineering
• Production Environment

Tools & Technologies

• Azure ML
• Azure Stream Analytics
• Cognitive Services
• Event Hubs
• Synapse
• Data Factory
• PyTorch
• TensorFlow