Virgin Atlantic - Apprentice - AI Engineer 2025
VIRGIN ATLANTIC AIRWAYS LIMITED
West Sussex (RH10 9DF)
Closes in 20 days (Sunday 20 July 2025)
Posted on 30 June 2025
Contents
Summary
We believe the best jobs make you fly—so what better way to launch your career than with a 24-month Virgin Atlantic Apprenticeship in Analytics, Data and AI? You’ll gain hands-on experience, industry-recognised qualifications, and make a real impact from day one.
- Wage
-
£26,255 a year
- Training course
- Machine learning engineer (level 6)
- Hours
-
Monday - Friday, 9.00am - 5.30pm, but a degree of flexibility will be required.
37 hours 30 minutes a week
- Start date
-
Monday 13 October 2025
- Duration
-
2 years
- 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
- Support the development and deployment of machine learning models using Python and Databricks
- Conduct data preparation and exploratory analysis to understand structures, trends, and anomalies
- Collaborate with data scientists and engineers to select appropriate modelling techniques
- Communicate technical insights through visualisations and business storytelling
- Apply best practices for responsible and ethical AI, including fairness and transparency
- Contribute to model explainability through tools such as SHAP and feature importance plots
- Engage with Databricks and supporting cloud platforms like Azure and adopt MLOps practices for scalable AI workflows
- Explore automation using Microsoft Copilot and OpenAI tooling
- Apply learnings from the Level 6 apprenticeship directly to project work at Virgin Atlantic
Where you'll work
Company Secretariat - The VHQ
Fleming Way
Crawley
West Sussex
RH10 9DF
Training
Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.
College or training organisation
CAMBRIDGE SPARK LIMITED
Your 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.
Your training plan
- The course runs for 24 months at our Crawley, West Sussex, VHQ
- After that, it’ll be time to put all your training into practice. The sky really is the limit
Requirements
Essential qualifications
GCSE in:
- English (grade Grade C/4 or above)
- Maths (grade Grade C/4 or above)
- Science (grade Grade C/4or above)
Let the company know about other relevant qualifications and industry experience you have. They can adjust the apprenticeship to reflect what you already know.
Skills
- Communication skills
- IT skills
- Attention to detail
- Organisation skills
- Problem solving skills
- Number skills
- Analytical skills
- Logical
- Team working
- Creative
- Initiative
- Patience
Other requirements
Before you apply, there are a couple of things we’d love you to consider. You’ll need strong attention to detail and a hands-on mindset—you’ll be working with complex data systems and production-grade tooling that require real precision (this might not be aircraft parts, but we treat data with the same level of care!).
About this company
Virgin Atlantic took off in 1984 when Richard Branson set out to shake up the aviation industry—and we’ve been doing things differently ever since. What began with a single 747, one route, and a small but mighty team has grown into an international airline with a global network and thousands of passionate people behind it. But our story has never just been about planes—it’s about people. From the very beginning, we’ve believed that travel should feel exciting, personal, and filled with possibility. That belief still drives us today as we work towards our mission: to become the most loved travel company. Whether we’re designing innovative customer experiences, pushing boundaries in sustainability, or building inclusive teams that reflect the world we serve, we’re united by a shared spirit of adventure, heart, and humanity. Because at Virgin Atlantic, we don’t just fly people from A to B—we help them take off in every sense. We’re not just your average airline. When it comes to our people, they’re a passionate lot, united in creating something different. It’s always been like this. It’s in our DNA, and it was ignited within us from the moment we started flying.
After this apprenticeship
You’ll complete the programme with more than just a sense of achievement. By the end of the 24 months, you’ll have built real-world experience applying AI and machine learning to solve business challenges, developed technical fluency in tools like Databricks and Azure, and earned a Level 6 AI Engineer qualification from Cambridge Spark.
In return for your hard work, we’ll support you every step of the way—so you can gain the skills, confidence, and connections you need to launch a meaningful career in data, analytics, or AI at Virgin Atlantic and beyond.
Ask a question
The contact for this apprenticeship is:
CAMBRIDGE SPARK LIMITED
The reference code for this apprenticeship is VAC1000327556.
Apply now
Closes in 20 days (Sunday 20 July 2025)
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After signing in, you’ll apply for this apprenticeship on the company's website.