Business Explorer, AI Expert Apprentice - Manchester
BT GROUP PLC
Salford (M3 5JL)
Closes in 16 days (Sunday 22 February 2026)
Posted on 2 February 2026
Contents
Summary
We’re growing our AI capabilities and we’re looking for curious problem solvers who love turning data into decisions. If you enjoy spotting patterns, learning to write code, and testing ideas with evidence, this is where you’ll make a difference.
- Wage
-
£23,810 a year
- Training course
- Machine learning engineer (level 6)
- Hours
-
Monday to Friday, 9.00am to 5.00pm (with some flexibility dependent on your team).
37 hours 30 minutes a week
- Start date
-
Monday 7 September 2026
- Duration
-
2 years 4 months
- Positions available
-
2
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
During this programme, you’ll work in key areas such as below work as part of team to become expert-level data and AI talent:
- AI Adoption & Enablement – helping shape, test and improve ML models, data pipelines, or automation solutions
- Data & Insights – partnering with product, operations or customer facing teams to understand real problems AI can help solve
- Customer Experience – trial and build AI assistants and knowledge tools to enable frontline or sales teams; capture feedback and measure value
- Transformation Programmes – contribute to change initiatives, tracking outcomes and risks, and presenting recommendations that stick
Where you'll work
New Bailey Office
4 Stanley Street
Salford
M3 5JL
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)
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
As an AI Expert apprentice, you’ll study for recognised apprenticeship (Level 6 AI/ Machine Learning Engineer). You’ll spend a minimum of 20% of your time learning and studying. After you have successfully completed your apprenticeship qualification and scheme, we’ll look to support you in securing a role that is best aligned to your strengths and interests.
Requirements
Essential qualifications
GCSE in:
- Any (grade 4)
- Any (grade 4)
- Any (grade 4)
- English (grade 4)
- Maths (grade 4)
A Level in:
- Any STEM Subject (grade C)
- Any STEM Subject (grade C)
Share if you have other relevant qualifications and industry experience. The apprenticeship can be adjusted to reflect what you already know.
Skills
- Communication skills
- Attention to detail
- Problem solving skills
- Number skills
- Analytical skills
- Logical
- Team working
- Initiative
Other requirements
- All applicants will need a full UK Right to Work for the duration of 30 months of their scheme without this we cannot accept an application
- Unfortunately do we not offer sponsorship for any of our Early Career roles
About this employer
You’re not just looking for a career, you’re looking to make a difference. Millions of people rely on us every day to help them live their lives, power their businesses, and keep their public services running. We connect friends to family, clients to colleagues, people to possibilities. We keep the wheels of business spinning, and the emergency services responding. And we use the power of technology to help solve big challenges, like climate change and cyber security. From day one, you’ll have a voice at BT Group. You’ll get stuck in to tough challenges, pitching ideas and making things happen. You won’t be alone though: we’ll be there with help and support through you’re learning and development. You’ll make great friends, discover new talents, and feel part of something exhilarating. This is your chance to make a real difference to the world. Grab it.
https://jobs.bt.com/content/Apprenticeships---BT-Group/ (opens in new tab)
Company benefits
- Discounts on EE & BT products
- Online GP
- Paid carer’s leave
- Support in carving your own career path
- Volunteering days
- Optional Private Healthcare and Dental
Disability Confident
A fair proportion of interviews for this apprenticeship will be offered to applicants with a disability or long-term health condition. This includes non-visible disabilities and conditions.
You can choose to be considered for an interview under the Disability Confident scheme. You’ll need to meet the essential requirements to be considered for an interview.
After this apprenticeship
On completion of your programme, your future role will depend on the opportunities available when you assimilate, but we will support you throughout this process to help identify and secure a suitable position.
Ask a question
The contact for this apprenticeship is:
QA LIMITED
The reference code for this apprenticeship is VAC2000012168.
Apply now
Closes in 16 days (Sunday 22 February 2026)
After signing in, you’ll apply for this apprenticeship on the company's website.