Senior Machine Learning Engineer - Department for Business and Trade - G7

Location

Belfast, Birmingham, Cardiff, Darlington, Edinburgh, London, Manchester

About the job

Job summary

The Department for Business, Innovation, Science and Trade (BIST) helps businesses invest, innovate, export and grow across the UK. The Digital, Data and Technology (DDaT) directorate helps to deliver this by designing, building and running the services, platforms and technology that businesses and colleagues rely on every day. 

Our teams work on a wide range of services and technologies, including: 

• digital services that help businesses access government support, guidance and opportunities 
• data and AI products that support decision making and enable innovation 
• internal tools and services that help colleagues work more effectively 
• secure technology and platforms that support critical government services 
• cyber security services that protect systems, data and users 

By joining DDaT, you'll work on services used by businesses, colleagues and citizens across the UK. You'll be part of multidisciplinary teams focused on improving services, solving complex problems and delivering better outcomes for users. 

We are committed to creating an inclusive and supportive workplace. In recognition of this, we were named Best Public Sector Employer at the Women in Tech Employer Awards 2025! 

Job description

The Senior Machine Learning Engineer plays a leading role in building the department’s machine learning engineering capability, turning data science and AI opportunities into secure, production-grade services that meet user needs and can be operated, assured and improved over time. 

In this role, you will work across multidisciplinary teams, collaborating with data scientists, software engineers, platform engineers, product managers, user-centred design colleagues and senior stakeholders to translate complex organisational challenges into scalable and sustainable machine learning solutions. You will provide technical direction on machine learning engineering, helping teams make effective architectural and engineering decisions and ensuring solutions are secure, reliable and designed for production environments. 

You will lead work across the full machine learning lifecycle, from data preparation, feature engineering and model development through to deployment, monitoring and continuous improvement. You will champion strong software engineering and MLOps practices, including automated testing, version control, CI/CD, model monitoring and assurance, helping establish consistent engineering standards for machine learning products. 

As a senior practitioner, you will help shape the department’s approach to machine learning and AI by turning good practice into usable standards, patterns and operating models. You will identify opportunities where these technologies can deliver value, provide technical leadership and assurance, promote responsible and ethical AI, and develop machine learning capability through coaching, knowledge sharing and continuous improvement. 

The post will also contribute to the development of the department’s machine learning engineering profession, helping define what good looks like for production machine learning, responsible AI delivery and reusable engineering practice across teams. 

Main responsibilities  

You will: 

• Lead the design, development and deployment of complex machine learning solutions, providing technical direction and ensuring solutions meet user and organisational needs
• Provide technical leadership across the machine learning lifecycle, from data preparation and feature engineering through model development, evaluation, deployment, monitoring and continuous improvement
• Set and champion engineering standards and best practice for machine learning development, including testing, version control, code review, documentation, security and maintainability
• Lead the adoption and development of MLOps practices, including CI/CD, automated testing and deployment, model monitoring and reproducible machine learning workflows
• Influence the department’s strategic approach to machine learning and AI, identifying opportunities for innovation and helping translate organisational priorities into deliverable technical solutions
• Provide technical guidance and assurance on machine learning solutions, making informed decisions around architecture, scalability, performance, reliability and technical risk
• Work collaboratively across multidisciplinary teams, influencing technical and non-technical stakeholders and communicating complex machine learning concepts clearly to support effective decision-making
• Champion responsible, ethical and secure AI, ensuring fairness, transparency, privacy, bias, cyber security, data protection and appropriate model assurance are embedded throughout the development lifecycle
• Build machine learning engineering capability across the organisation by sharing knowledge, coaching practitioners, strengthening communities of practice and helping establish the standards, patterns and learning environment needed for sustainable delivery
• Promote innovation and continuous improvement, evaluating emerging approaches and technologies and considering where they could improve the department’s machine learning products and services 

Person specification

It is essential that you have: 

• Experience of leading the end-to-end machine learning lifecycle, from working with large or complex datasets and feature engineering through to model development, deployment, monitoring, assurance, incident response and continuous improvement in production environments (Lead Criteria)
• Significant experience of programming and software engineering, using languages such as Python or similar to design and develop high-quality, scalable and maintainable machine learning solutions, applying strong engineering practices including version control, testing and code review
• Strong experience of designing, developing and evaluating machine learning models, applying appropriate machine learning, statistical and mathematical techniques to solve complex real-world problems
• Experience of MLOps and CI/CD practices, including automated testing and deployment, model monitoring and reproducible workflows, to support reliable and sustainable machine learning services
• Experience of providing technical leadership and setting technical direction, making informed decisions on architecture, engineering approaches, scalability and technical risk while establishing and championing high engineering standards
• Experience of influencing strategy and collaborating across multidisciplinary teams, translating organisational priorities into technical approaches, communicating complex information effectively to technical and non-technical stakeholders, and helping build organisational capability through standards, coaching and reusable engineering practices 

It is desirable that you have: 

• Experience of designing and deploying machine learning solutions within cloud environments, such as AWS, Azure or GCP, including containerisation, infrastructure as code, observability, security controls and operational support
• Experience of responsible AI and model assurance, including applying approaches relating to data ethics, privacy, fairness, transparency and bias