Machine Learning Engineer, Vice President

• Join us as a Machine Learning Engineer
• In this role, you'll drive the design, development and operationalisation of machine learning and AI solutions, enabling the delivery of resilient, scalable and innovative solutions using both Generative AI and traditional Machine Learning techniques
• Day-to-day, act as a technical expert, building and deploying production-grade ML and AI systems while championing engineering best practices across the team
• If you are ready for a new challenge and are passionate about turning cutting-edge machine learning and AI capabilities into robust business solutions, this could be the role for you
• We're offering this role at vice president level

We're looking for someone who can work with our Data Scientists to understand business requirements and translate them into scalable machine learning and AI solutions. You'll work closely with data scientists, engineers, architects and business stakeholders to deliver reliable and secure products that support our business strategy and customer outcomes.

You'll stay curious about emerging technologies and industry developments, helping to identify opportunities to improve our machine learning and AI platforms, tooling and delivery capabilities.

You will also be responsible for:

• Designing, building, deploying and maintaining machine learning, Generative AI and Agentic AI solutions in production environments
• Implementing robust ML engineering and MLOps practices, including CI/CD pipelines, automated testing, model monitoring and observability
• Developing scalable data pipelines, feature engineering processes and reusable platform components to support model deployment and operation
• Working collaboratively within Agile, multi-disciplinary teams to deliver value through iterative development and continuous improvement
• Ensuring solutions meet engineering, security, governance, risk and regulatory requirements throughout the software and model lifecycle

To be successful in this role, you'll need experience designing, building and supporting production machine learning and AI solutions within a modern engineering environment. You'll ideally hold an undergraduate or master's degree in a quantitative, engineering or computer science discipline, or demonstrate equivalent practical experience.

You'll also need strong software engineering experience, including cloud-native development, data engineering and machine learning operations. We'll look to you to demonstrate technical leadership, a passion for engineering excellence and the ability to mentor others while continually developing your own skills. Ability to self-manage, take initiative and demonstrate a proactive approach to delivering high-quality engineering outcomes is also required.

You will also need:

• Proven experience delivering complex machine learning and AI solutions into production and communicating technical concepts effectively to both technical and non-technical audiences
• Strong software engineering skills, including object-oriented programming, API development, testing frameworks, source control and modern development practices
• Experience developing, deploying and supporting Generative AI and Agentic AI solutions, including orchestration frameworks, evaluation methodologies and operational monitoring as well as traditional Machine Learning techniques
• Expertise in MLOps practices, including model deployment, CI/CD, infrastructure as code, observability, model monitoring and performance optimisation. Knowledge of containerisation and orchestration technologies such as Docker and Kubernetes
• Experience working with cloud technologies such as AWS, alongside strong proficiency in Python, SQL and distributed data processing technologies
• Effective verbal and written communication skills, with the ability to adapt communication style for different audiences and stakeholder groups
• Additional skills that would be beneficial: RAG, semantic search and vector database experience, Knowledge graph and retrieval optimisation, MCP and tool-calling integration patterns for AI agents, LLM evaluation, observability and production monitoring

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