AI Engineer Level 6 Career Changer Apprenticeship (Accenture)
ACCENTURE (UK) LIMITED
Newcastle upon Tyne (NE27 0QQ)
Closes on Saturday 28 February 2026
Posted on 24 October 2025
Contents
Summary
Thinking of a new career in AI? We have created this Level 6 Career Changer apprenticeship to support your future career. You will design and build software solutions that harness the power of AI tools and modern development practices.
- Wage
-
£32,028 a year
Check minimum wage rates (opens in new tab)
When you join, you'll start on a salary of £32,028 per year plus access a competitive range of benefits.
- Training course
- Machine learning engineer (level 6)
- Hours
-
Monday to Friday.
Shifts to be confirmed.
37 hours 30 minutes a week
- Start date
-
Monday 4 May 2026
- Duration
-
1 year 4 months
- Positions available
-
10
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
You’ll receive a blend of structured learning, hands-on experience, and tailored apprenticeship training to help you grow into a confident tech professional. From day one, you’ll be supported by mentors, coaches, and a dedicated Accenture team focused on your development and wellbeing.
You’ll get access to:
- Core Apprentice Training: Your journey will begin with a comprehensive 5-week training programme designed to give you a strong foundation in both technical and professional skills. You’ll explore the fundamentals of AI, and data handling while also learning how modern engineering teams operate—using agile methods, collaboration tools, and real-world problem solving.
- Project-based learning: You’ll work on real challenges for real clients. With support from experienced teams, you’ll gain practical knowledge and grow your confidence on the job.
- Technology Exposure: Every project is different—and so is the tech. You’ll get hands-on experience with a wide range of technologies and systems, along with training to help you understand and implement them effectively.
- Professional Development: We invest in your continuous growth through our extensive learning portal, offering curated pathways, classroom-based and self-paced courses, and on-demand resources to keep your skills sharp throughout your apprenticeship.
Apprenticeship Training: You’ll study towards the Level 6 (Degree Equivalent) AI Engineer Apprenticeship through BPP University. The programme lasts for 18 months. You’ll spend one day a week at university and four days working on client projects—applying what you learn in real time. On successful completion, you’ll have the opportunity to move into a permanent role at Accenture.
Where you'll work
5 Quick Silver Way
Cobalt Business Park
Newcastle upon Tyne
NE27 0QQ
Training
Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.
Training provider
BPP UNIVERSITY LIMITED
Training course
Machine learning engineer (level 6)
Understanding apprenticeship levels (opens in new tab)
What you'll learn
Course contents
- 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
More training information
BPP apprenticeship training programmes are delivered virtually by our fully qualified and industry-experienced training team. Using their expert knowledge, we’ve purposefully built our programmes around the real-world use of modern technology, so that the skills we create can be directly applied in the workplace.
Throughout the apprenticeship, learners receive coaching, help and guidance from a dedicated team who are there to ensure they get the most from their work experience.
Requirements
Essential qualifications
GCSE in:
Degree in:
Share if you have other relevant qualifications and industry experience. The apprenticeship can be adjusted to reflect what you already know.
Skills
- Communication skills
- A continuous desire to learn
- Flexibility and resilience
- Fast-paced environment
- Teamwork skills
- Commitment and motivation
Other requirements
• You don’t need a background in tech—just a strong interest in software engineering and AI, and a desire to learn something new. This is a chance to reskill and transition into a tech career, with hands-on experience, structured learning, and support every step of the way. Plus, on successful completion, you’ll have the opportunity to top up to a MSc Degree, fully funded by Accenture, further accelerating your career in tech.
About this employer
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 801,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.
After this apprenticeship
Typical roles include: AI Engineer, Data Scientist, Machine Learning Engineer, AI Solutions Architect.
Ask a question
The contact for this apprenticeship is:
BPP UNIVERSITY LIMITED
The reference code for this apprenticeship is VAC1000348102.
Apply now
Closes on Saturday 28 February 2026
Sign in with your GOV.UK One Login to apply.
After signing in, you’ll apply for this apprenticeship on the company's website.