AI Engineer Apprentice (Harsco Environmental)
Harsco Environmental
Rotherham (S60 1BW)
Closes in 18 days (Monday 29 September 2025)
Posted on 10 September 2025
Contents
Summary
You’ll work with our Digital team to develop AI solutions that solve real business challenges. This is a unique opportunity to gain hands-on experience in a growing field, as we invest in building a dedicated AI capability across the business. Ideal for someone with a passion for technology, data, and learning.
- Wage
-
£23,809 a year
- Training course
- Machine learning engineer (level 6)
- Hours
-
Monday to Friday
Shifts to be confirmed
37 hours a week
- Start date
-
Monday 6 October 2025
- Duration
-
1 year 6 months
- 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 is an exciting opportunity to join Harsco Environmental’ s Digital team as an AI Engineer Apprentice. As artificial intelligence becomes a key driver of innovation across industries, we are beginning a strategic focus on building a dedicated AI capability within the business. This role offers a unique chance to be part of that transformation from the ground up - supporting the Digital Architect and collaborating with developers and stakeholders to deliver AI solutions that create measurable business value.
- Support the design and development of AI and machine learning models
- Build and scale AI solutions using Azure cloud services
- Apply statistical methods to analyse and prepare data
- Contribute to innovation projects that drive business improvements
- Collaborate with developers, architects, and business stakeholders
- Participate in sprint reviews and demos to showcase progress
- Document models, data sources, and transformation logic
- Assist in preparing user manuals and training materials for AI tools
Where you'll work
The Point , Bradmarsh Way
Rotherham
S60 1BW
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
Machine Learning Engineer Level 6.
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
A Level in:
Share if you have other relevant qualifications and industry experience. The apprenticeship can be adjusted to reflect what you already know.
Skills
- Problem solving skills
- Analytical skills
- Basic programming knowledge
- Ability to work independently
- Collaborative approach to work
- Willingness to learn
- Fast paced environment
Other requirements
· Understanding of AI and machine learning concepts · Familiarity with Azure cloud services · Use of statistical methods and data analysis · Experience with version control tools like Git · Effective communication with technical and non-technical stakeholders · Curiosity about emerging technologies and AI trends · Willingness to learn and adapt in a fast-paced environment
About this employer
Harsco Environmental is a global leader in environmental solutions for the steel and metals industry. With a strong commitment to sustainability and innovation, the company delivers tailored services that span the entire production process—from scrap handling and inventory tracking to risk management and recovery. Harsco Environmental empowers its clients to operate more efficiently and responsibly, making a meaningful impact on industrial environmental performance worldwide.
After this apprenticeship
As artificial intelligence continues to reshape industries, now is the ideal time to join our journey. AI is a rapidly growing area of demand, and we’re at the beginning of a strategic shift - actively investing in a dedicated group of AI resources to drive innovation across the business. This role offers a unique opportunity to be part of that transformation from the ground up, contributing to meaningful projects while developing skills that are increasingly vital in the digital future.
Ask a question
The contact for this apprenticeship is:
BPP UNIVERSITY LIMITED
Nabila Lotfy
Nabilalotfy@bpp.com
01133500333
The reference code for this apprenticeship is VAC1000341517.
Apply now
Closes in 18 days (Monday 29 September 2025)
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