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2027 AI Engineer Apprentice - Level 6 - Crawley
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2027 AI Engineer Apprentice - Level 6 - Crawley
THALES UK LIMITED
Crawley (RH10 9HA)
Closes on Wednesday 17 February 2027
Posted on 5 October 2026
Summary
Working as part of our AI and engineering community, you will contribute to live projects across the machine learning lifecycle, collaborating with software, data, systems and domain specialists to turn needs into secure, maintainable solutions.
Wage
£24,000 a year
Training course
Machine learning engineer (level 6)
Hours
Flexible working. Monday - Thursday, 8 hours per day. Friday, 5 hours. 37 hours a week
Start date
Monday 6 September 2027
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
- Preparing, exploring and validating data for machine learning use cases
- Developing and evaluating machine learning models and AI-enabled software components
- Writing clean, tested and documented code using appropriate engineering tools and practices
- Supporting model integration, deployment, monitoring and performance improvement
- Contributing to technical reviews, experiments and structured problem solving
- Working with experienced engineers and researchers on live projects
Where you'll work
Manor Royal
Crawley
RH10 9HA
Training
Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.
Training provider
QA LIMITED
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.
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Training schedule
You will study towards a Level 6 AI Engineer apprenticeship with QA, developing the knowledge, skills and behaviours required to build and apply artificial intelligence responsibly in an engineering environment. The programme is delivered through a virtual learning model, combining: Live virtual lectures (28 days), Online learning resources, Self-directed study.
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More training information
This programme will take you through the AI lifecycle, from design and build, through to deployment and governance – providing a strong foundation for your role within Thales. The programme includes the Microsoft certified Azure AI Engineer Associate qualification, in addition to the Level 6 Machine Learning Engineer Apprenticeship qualification.
Requirements
Essential qualifications
- GCSE in: 5 GCSE's Including English and Maths (grade Grade 9-4 (A-C))
- A Level in: 3 A Levels in Maths and a relevant subject (grade C or above with a B in Maths)
Share if you have other relevant qualifications and industry experience. The apprenticeship can be adjusted to reflect what you already know.
Skills
- Attention to detail
- Problem solving skills
- Number skills
- Analytical skills
- Logical
- Team working
- Initiative
- Patience
Other requirements
Because of the work we do at Thales, all roles are subject to security requirements. To be considered for this position, you must have the permanent right to work in the UK and be able to successfully complete and maintain UK Government security checks, including Baseline Personnel Security Standard (BPSS) screening and Security Clearance (SC), which is a government background check, before starting employment. To be eligible for SC clearance, you will typically need to have lived in the UK continuously for the last five years. As part of the vetting process, you will be asked to provide evidence of your identity, right to work
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