Software Engineer Apprentice

VE3; City Of London; Hybrid; GBP 20,000 - 30,000; Full time

Job summary

Join a leading company as a Software Engineer Apprentice in a hybrid role based in London. This advanced apprenticeship focuses on practical AI and ML training, offering hands-on experience in model development, data engineering, and AI-powered solutions, complemented by structured academic instruction.

Qualifications

• Degree in Computer Science, AI, Data Science, Mathematics, or Software Engineering required.
• Eligibility for Level 6 or 7 apprenticeship is necessary.
• Programming skills in Python and familiarity with ML libraries like TensorFlow or PyTorch desired.

Responsibilities

• Assist in developing and tuning machine learning models and scalable data pipelines.
• Conduct experiments to evaluate model performance and debug behavior.
• Collaborate in building AI-powered APIs and integrate backend services.

Skills

Python

Pandas

NumPy

Scikit-learn

TensorFlow

PyTorch

Data visualization

Education

Bachelor’s or Master’s degree in Computer Science

Artificial Intelligence

Data Science

Mathematics

Software Engineering

Related STEM field

Tools

Jupyter Notebooks

Git

GitHub

Job description

Software Engineer Apprentice

Level: 6 (Degree) or 7 (Postgraduate)

Location: Hybrid (London-based)

Job Purpose

The AI Engineer Apprenticeship is an advanced, hands-on training programme designed for individuals passionate about artificial intelligence and machine learning. Whether you are a recent graduate or in your final year of studies, 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 while receiving structured academic instruction and mentorship. The programme combines practical work experience with formal training aligned with national apprenticeship standards for Artificial Intelligence (Level 6) or Data Science (Level 7).

Requirements

Key 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 or ongoing) in:
  

• Computer Science
  

• Artificial Intelligence
  

• Data Science
  

• Mathematics
  

• Software Engineering
  

• Or a related STEM field
  

• Eligibility to enrol on a Level 6 or Level 7 AI/ML/Data Science apprenticeship programme.
  

Core Skills & Competencies

Technical Skills

• 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