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Software Engineer Apprentice

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Job Title: Software Engineer Apprentice
Location: Maidenhead, United Kingdom
Position Type: Full-time
Experience Level: 0-2 years
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.
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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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.


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Required Qualifications
A Bachelor’s or Master’s degree (completed) in:
- Computer Science
- Artificial Intelligence
- Data Science
- Mathematics
- Software Engineering
- Or a related STEM field
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.
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