Trainee AI Programmer

Trainee AI Engineer – No Experience Needed

Future-proof your career in Artificial Intelligence – starting today.

Looking for a career change? Currently employed but want something better? Or maybe you're between jobs and ready for a fresh start? ITOL Recruit's AI Traineeship is designed to get you into one of the fastest-growing industries with zero experience required.

Train online at your own pace and land your first AI Engineer role in 1-3 months.

Please note this is a training course and fees apply

Job guaranteed - complete the programme and get a job or get your money back.

Our candidates earn £28,000-£45,000.

Why AI?

AI is reshaping every industry you can think of. Healthcare, finance, retail, and manufacturing – they’re all scrambling for skilled professionals.

The demand far outstrips supply, which means excellent salaries, flexible working arrangements, and genuine job security.

How It Works

Step 1 – AI Engineering Fundamentals

Start with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering.

Step 2 – Data Fundamentals

Understand the data workflow, from collection to cleaning, and learn how to prepare data for AI applications.

Step 3 – Notebooks & IDEs

Get hands-on with industry-standard tools like Jupyter Notebooks and VS Code to develop AI systems.

Step 4 – Python Programming

Master Python, covering everything from the basics to object-oriented programming (OOP).

Step 5 – Python Streamlit Project

Apply your Python skills by building a car price prediction app using Python and Streamlit.

Step 6 – Python for Data

Learn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation.

Step 7 – AI Sentiment Analysis Project

Work with Hugging Face to build a sentiment analysis classifier using real-world AI techniques.

Step 8 – AI Prompt Engineering

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs.

Step 9 – Retrieval-Augmented Generation (RAG)

Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases.

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