Trainee AI Programmer
Job description
Texte original importé depuis Reed
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.
Step 10 – AI Specialised Customer Service Chatbot Project
Combine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases.
Step 11 – Machine Learning Fundamentals
Understand machine learning principles and algorithms, and how to train and test models using scikit-learn.
Step 12 – Machine Learning Project
Put your machine learning knowledge into practice with a hands-on project.
Step 13 – AI & Data Ethics
Study the ethical considerations in AI, including issues of bias, fairness, and data privacy.
Step 14 – Oral Exam
Complete a virtual oral exam to assess your understanding and ability to apply your learning.
Step 15 – AWS Certified Cloud Practitioner
Finish with the AWS Certified Cloud Practitioner course and exam to gain essential cloud computing knowledge.
What You Get
· 100% online, self-paced training
· Microsoft AI-900 certification included
· 1-to-1 tutor and recruitment support
· Real-world project experience
· Job guarantee – get a job or your money back
· Starting salary of £28,000–£45,000
We Get You Hired
We're not new to this. ITOL Recruit has 15+ years of experience and has placed over 5,000 people into new roles.
Our job programmes include certified tutors, UK-accredited qualifications, and one-on-one support from a recruitment adviser focused on placing you.
We don't believe in empty promises. Complete our programme, follow the process, and if you don't land a job, you get your money back.
"Five months from complete beginner to AI engineer. Best decision I ever made." – Jamie W., now working as a Junior AI Engineer in London
Ready to Start?
If you’re motivated, curious, and excited about technology, we’ll help you turn that into a career you can be proud of.
Apply now, and one of our expert Career Advisors will be in touch within 4 working hours to guide you through your next steps.
Key skills extracted
Our AI analysed the job to identify the required skills.
Our tips for applying
5 recommandations générées par notre IA pour maximiser vos chances.
⭐ Showcase your AWS Certified Cloud Practitioner certification prominently in your CV header or skills section, as the advert lists this as the programme's capstone credential.
📊 Quantify your project work: e.g. 'Built a car price prediction app using Python and Streamlit, achieving 87% model accuracy on a 10,000-row dataset' to demonstrate practical output.
🎯 List each completed project (sentiment analysis classifier, RAG chatbot, ML project) as a separate CV entry under a 'Projects' section, naming the tools used (Hugging Face, scikit-learn, vector databases) to pass ATS filters.
🌐 Highlight your understanding of AI & Data Ethics — bias, fairness, and data privacy — as this is increasingly required by UK employers under GDPR and emerging AI regulation frameworks.
🤝 Reference your oral exam completion as evidence of the ability to articulate technical concepts clearly, which is a differentiator for junior AI roles where communication of model outputs to stakeholders is valued.
Bullets CV suggérés
3 bullets générés par notre IA pour cette offre, alignés sur ses mots-clés ATS.
Ajoutez ces 3 bullets sous votre expérience la plus récente :
- •Developed a car price prediction web application using Python and Streamlit, applying object-oriented programming principles and achieving a mean absolute error of under £1,200 on a 5,000-record dataset.
- •Built an AI-powered customer service chatbot combining prompt engineering and RAG techniques with a vector database knowledge base, reducing simulated query resolution time by 40% versus a baseline keyword search system.
- •Completed AWS Certified Cloud Practitioner certification alongside 14 AI engineering modules, demonstrating proficiency in neural networks, scikit-learn model training, and data preprocessing with NumPy and Pandas within a 10-week self-paced programme.
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Votre lettre de motivation est prête
Nous avons rédigé une lettre pour ITOL Recruit. Découvrez l'ouverture, puis débloquez la version complète personnalisée.
Aperçu — adapté à ITOL Recruit
Dear Hiring Manager,
ITOL Recruit's AI Traineeship stands out as one of the few programmes that combines hands-on Python development, Retrieval-Augmented Generation, and AWS cloud certification within a single, structured pathway — which is precisely why I am applying for the Trainee AI Engineer role. The curriculum's focus on real-world projects, including a sentiment analysis classifier built with Hugging Face and an AI-powered customer service chatbot using vector databases, aligns directly with the applied engineering skills I am committed to developing.
My background in self-directed learning and problem-solving has prepared me to work through the 15-module programme at pace. I am confident in my ability to engage with the data fundamentals, machine learning principles, and AI ethics components, and to translate that knowledge into deployable solutions using tools such as scikit-learn, Pandas, and Streamlit.
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Likely interview questions
10 questions générées à partir de cette offre.
Technical
- ›Can you explain the difference between supervised and unsupervised machine learning, and give an example of when you would use each?
- ›Walk me through how Retrieval-Augmented Generation (RAG) works and describe a use case where it outperforms a standard LLM.
- ›What is the role of vector databases in an AI pipeline, and which vector database did you work with during your training?
- ›How would you approach cleaning and preparing a raw dataset in Python using Pandas before feeding it into a scikit-learn model?
- ›What are the key considerations when crafting effective prompts for a large language model, and how do you evaluate prompt quality?
Behavioural
- ›Tell me about a time you had to learn a completely new technical skill independently — how did you structure your learning?
- ›Describe a project where you encountered unexpected results or errors. How did you diagnose and resolve the problem?
- ›Give an example of when you had to manage your time across multiple tasks or modules without direct supervision.
- ›Tell me about a situation where you identified an ethical concern in a process or dataset. What did you do?
- ›Describe a time you had to explain a technical concept to someone without a technical background. How did you approach it?
Exemples de réponses STAR
Réponses modèles avec la méthode Situation-Tâche-Action-Résultat. À adapter à votre vécu.
Tell me about a time you had to learn a completely new technical skill independently — how did you structure your learning?
Describe a project where you encountered unexpected results or errors. How did you diagnose and resolve the problem?