Education:
Degree or Postgraduate
Location:
Hybrid (Maidenhead-based)
Job Purpose
The
AI Engineer Apprenticeship
is an advanced, hands-on training programme designed for individuals passionate about artificial intelligence and machine learning. This role offers the opportunity to work alongside seasoned AI engineers, data scientists, and product teams, contributing to the development of real-world AI solutions.
You will support the development of data pipelines, machine learning models, and prototype applications .
RequirementsKey Responsibilities
Model \& Data Pipeline Development
Assist in collecting, cleaning, validating, and preparing data for training and evaluation.
Support the design, development, and tuning of machine learning and deep learning models.
Contribute to scalable and reusable data pipelines using modern ML workflows.
Experimentation \& Evaluation
Conduct experiments and benchmarking exercises to test model performance.
Perform error analysis, feature importance, and other model diagnostics.
Track and log training/testing outcomes to support reproducibility and model versioning.
Engineering Contributions
Help build and integrate AI-powered APIs, scripts, and microservices.
Collaborate on backend services and model deployment in dev/test environments.
Use Git, CI/CD tools, and containerization (e.g., Docker) to maintain codebase quality.
Applied AI Domains
Work on projects that involve Natural Language Processing (NLP), Computer Vision, Generative AI, or Recommendation Systems.
Support annotation, feature engineering, and augmentation tasks where necessary.
Documentation \& Collaboration
Write clear, well-organized documentation for code, models, datasets, and project workflows.
Participate in team meetings, sprint planning, and code reviews.
Engage with mentors to reflect on progress, set learning goals, and track outcomes.
Required Qualifications
A Bachelor’s Or Master’s Degree (completed) In
Computer Science
Artificial Intelligence
Data Science
Mathematics
Software Engineering
Technical Skills
Core Skills \& Competencies
Programming proficiency in Python and common ML libraries such as:
Pandas, NumPy, Scikit-learn
TensorFlow, PyTorch, or similar
Experience with Jupyter Notebooks and version control (Git/GitHub)
Basic understanding of supervised/unsupervised learning, neural networks, or clustering
Analytical Abilities
Ability to interpret data trends, visualize outputs, and debug model behaviour