Machine Learning Engineer - Apprentice

Summary

This apprenticeship is a role within the Civil Service. To see full details of the apprenticeship click on ‘apply’ to go to the Civil Service Jobs website.

Wage

£29,795 a year

Training course

Machine learning engineer (level 6)

Hours

Click apply to see full details of the working week for this apprenticeship.

37 hours a week

Start date

Tuesday 1 September 2026

Duration

2 years

Positions available

1

Work

Most of your apprenticeship is spent working. You’ll learn on the job by getting hands-on experience.

What you'll do at work

This apprenticeship is a role within the Civil Service. To see full details of the apprenticeship click on ‘apply’ to go to the Civil Service Jobs website.

Where you'll work

You can select which locations you want to apply for in your application on Find an apprenticeship.

This apprenticeship is available in these locations:

• 23 Stephenson Street, Birmingham, B2 4AJ
• Unit 1, 60 Old Hall Street, Liverpool, L3 9PP

Training

Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.

Training provider

To be confirmed

Training course

Machine learning engineer (level 6)

What you'll learn

• Assess vulnerabilities of the proposed design, to ensure that security considerations are built in from inception and throughout the development process.
• Translate business needs and technical problems to scope machine learning engineering solutions.
• Select and engineer data sets, algorithms and modelling techniques required to develop the machine learning solution.
• Apply methodologies and project management techniques for the machine learning activities.
• Create and deploy models to produce machine learning solutions.
• Document the creation, operation and lifecycle management of assets during the model lifecycle.
• Apply techniques for output model testing and tuning to assess accuracy, fit, validity and robustness.
• Assess system vulnerabilities and mitigate the threats or risks to assets, data and cyber security.
• Refine or re-engineer the model to improve solution performance.
• Apply techniques for monitoring models in the live environment to check they remain fit for purpose and stable.
• Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process.
• Apply machine learning and data science techniques to solve complex business problems.
• Track and test continual learning models.
• Analyse test data, interpret results and evaluate the suitability of proposed solutions both new and inherited models, considering current and future business requirements.
• Identify, consider and advocate for ML solutions to deliver an environmental and operational sustainable outcome.
• Transition prototypes into the live environment.
• Complete audit activities in compliance with policies, governance, industry regulation and standards.
• Consider the risks with using digital and physical supply chains.
• Ensure the model capacity is scaled in proportion to the operating requirements.
• Support the evaluation and validation of machine learning models and statistical evidence to minimise algorithmic bias being introduced.
• Monitor data curation and data quality controls including for synthetic data.
• Identify and select the machine learning or artificial intelligence platform architecture and specific hardware, to contribute to solving a computational problem using allocated resources.
• Identify and embed changes in work to deliver sustainable outcomes.
• Monitor model data drift, using performance metrics to ensure systems are robust when moving outside of their domain of applicability.
• Develop a process to decommission assets in line with policy and procedures. Manage current and legacy models in line with industry approaches.
• Undertake independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances.
• Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers and multi-disciplinary teams with conflicting priorities, interests and timescales.
• Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information.
• Create and disseminate reports, presentations and other documentation that details the model development to confirm stakeholder approval for handover to implementation.
• Comply with equality, diversity, and inclusion policies and procedures in the workplace.
• Horizon scan to identify new technological developments that offer increased performance of data products.
• Apply Machine Learning principles and standards such as, organisational policies, procedures or professional body requirements.
• Integrate AI-based approaches, including those provided by third-party vendors’ Application Programming Interfaces, into existing and new processes.
• Proactive identification of the potential for automation for example through AI solutions embedded within tooling.
• Assess vulnerabilities of the proposed design, to ensure that security considerations are built in from inception and throughout the development process.
• Translate business needs and technical problems to scope machine learning engineering solutions.
• Select and engineer data sets, algorithms and modelling techniques required to develop the machine learning solution.
• Apply methodologies and project management techniques for the machine learning activities.
• Create and deploy models to produce machine learning solutions.
• Document the creation, operation and lifecycle management of assets during the model lifecycle.
• Apply techniques for output model testing and tuning to assess accuracy, fit, validity and robustness.
• Assess system vulnerabilities and mitigate the threats or risks to assets, data and cyber security.
• Refine or re-engineer the model to improve solution performance.
• Apply techniques for monitoring models in the live environment to check they remain fit for purpose and stable.
• Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process.
• Apply machine learning and data science techniques to solve complex business problems.
• Track and test continual learning models.
• Analyse test data, interpret results and evaluate the suitability of proposed solutions both new and inherited models, considering current and future business requirements.
• Identify, consider and advocate for ML solutions to deliver an environmental and operational sustainable outcome.
• Transition prototypes into the live environment.
• Complete audit activities in compliance with policies, governance, industry regulation and standards.
• Consider the risks with using digital and physical supply chains.
• Ensure the model capacity is scaled in proportion to the operating requirements.
• Support the evaluation and validation of machine learning models and statistical evidence to minimise algorithmic bias being introduced.
• Monitor data curation and data quality controls including for synthetic data.
• Identify and select the machine learning or artificial intelligence platform architecture and specific hardware, to contribute to solving a computational problem using allocated resources.
• Identify and embed changes in work to deliver sustainable outcomes.
• Monitor model data drift, using performance metrics to ensure systems are robust when moving outside of their domain of applicability.
• Develop a process to decommission assets in line with policy and procedures. Manage current and legacy models in line with industry approaches.
• Undertake independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances.
• Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers and multi-disciplinary teams with conflicting priorities, interests and timescales.
• Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information.
• Create and disseminate reports, presentations and other documentation that details the model development to confirm stakeholder approval for handover to implementation.
• Comply with equality, diversity, and inclusion policies and procedures in the workplace.
• Horizon scan to identify new technological developments that offer increased performance of data products.
• Apply Machine Learning principles and standards such as, organisational policies, procedures or professional body requirements.
• Integrate AI-based approaches, including those provided by third-party vendors’ Application Programming Interfaces, into existing and new processes.
• Proactive identification of the potential for automation for example through AI solutions embedded within tooling.

Training schedule

This training schedule has not been finalised. Check with this employer if you’ll need to travel to a college or training location for this apprenticeship.

Requirements

Desirable qualifications

A Level in:

English and Maths (grade C)

Share if you have other relevant qualifications and industry experience. The apprenticeship can be adjusted to reflect what you already know.

Skills

Click apply for details