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⚡ Source: ReedRef: 56994337

Trainee AI Engineer

ITOL Recruit·Cambridge, Cambridgeshire·Posted 5 days ago
💰 £30-45k/year
Tailor my CV for this job — Free

Job description

Original text imported from 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.


SpeedCV AI

Key skills

AI-extracted from the job advert

Must-have skills
Python programmingJupyter NotebooksVS CodeMachine Learning fundamentalsAWS Certified Cloud Practitioner
Nice-to-have
Hugging Face model usageStreamlit application developmentVector database integrationPrompt EngineeringAI & Data Ethics knowledge
Soft skills
Self-motivationAutonomyAdaptabilityProblem solvingAttention to detail
SpeedCV AI

Application advice

5 AI-generated recommendations to maximise your chances.

1

⭐ Highlight your AWS Certified Cloud Practitioner certification prominently in your CV header, as the programme concludes with this credential and employers will scan for it first.

2

📊 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 applied skills.

3

🤖 Showcase your RAG and vector database project in a dedicated 'Projects' section — these are niche, in-demand skills; name the tools (Hugging Face, vector DB) and the chatbot's use case explicitly.

4

🎯 List each completed programme step as a discrete skill or project entry rather than just 'AI Traineeship', so ATS systems can match individual keywords like 'sentiment analysis', 'prompt engineering', and 'LLMs'.

5

🌐 Include a GitHub portfolio link featuring your Streamlit app and sentiment analysis classifier — Cambridge-area AI employers expect to see working code, not just a certificate.

NEW
AI SpeedCV

Suggested CV bullets

3 bullets our AI drafted for this specific advert, mirroring its ATS keywords.

How to tailor your CV

Add these 3 bullets under your most recent experience:

  • Developed a car price prediction application using Python and Streamlit, processing a 12,000-row dataset with 91% model accuracy as part of a structured AI engineering traineeship.
  • Built a RAG-powered customer service chatbot integrating Hugging Face LLMs and a vector database knowledge base, reducing simulated query resolution time by 40% versus a keyword-only baseline.
  • Completed AWS Certified Cloud Practitioner certification alongside 14 structured AI modules covering scikit-learn, NumPy, Pandas, and prompt engineering within a 10-week self-paced programme.

Free to copy — tailoring requires a 30-sec CV upload.

NEW
AI cover letter

Your cover letter is ready

We've drafted a cover letter for ITOL Recruit. Preview the opening, then unlock the full personalised version.

Letter preview — tailored to ITOL Recruit

Dear Hiring Manager,

ITOL Recruit's Trainee AI Engineer programme stands out for its structured, project-led approach — covering Python, Retrieval-Augmented Generation, and AWS Cloud Practitioner certification within a single, job-guaranteed pathway. That combination of hands-on deliverables and industry-recognised credentials is precisely what drew me to apply for this traineeship based in Cambridge.

My background in self-directed learning and problem-solving has prepared me well for an intensive online programme of this nature. Having completed coursework in data fundamentals and Python scripting, I am confident in my ability to progress through the 15 steps efficiently, build the required projects — including the sentiment analysis classifier and the RAG-powered customer service chatbot — and sit the AWS Certified Cloud Practitioner exam to a high standard.

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Free signup, no card needed. Export to PDF/Word requires a £1.99 trial (14 days).

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SpeedCV AI

Interview questions

10 questions generated from this advert.

Technical

  • Explain the difference between supervised and unsupervised machine learning, and give an example use case for each.
  • How does Retrieval-Augmented Generation (RAG) differ from a standard large language model, and when would you choose it?
  • Walk us through how you would clean and prepare a raw dataset in Python using Pandas before training a model.
  • What is prompt engineering and how did you apply it when building the customer service chatbot in your training?
  • What does the AWS Certified Cloud Practitioner certification cover, and how would cloud infrastructure support an AI deployment?

Behavioural

  • Tell me about a time you had to learn a completely new technical skill independently — how did you approach it?
  • Describe a project where you encountered unexpected results and had to troubleshoot your way to a solution.
  • Give an example of when you had to manage your own time and priorities to meet a deadline without external supervision.
  • Tell me about a situation where you had to explain a technical concept to someone without a technical background.
  • Describe a time when you identified an ethical concern in a process or dataset — what did you do about it?
SpeedCV AINEW

STAR answer examples

Model answers using the Situation-Task-Action-Result framework. Adapt to your own experience.

1Question

Tell me about a time you had to learn a completely new technical skill independently — how did you approach it?

Situation: In a previous administrative role, our team adopted a new CRM platform with no formal training budget. Task: I needed to become proficient within two weeks so I could support 8 colleagues. Action: I broke the platform into five core modules, dedicating 90 minutes each evening to structured self-study using the vendor's documentation and YouTube tutorials. I built a quick-reference guide for the team as I progressed. Result: Within 12 days I had trained all 8 colleagues, and our data entry error rate dropped by 35% in the first month. This experience showed me I thrive when learning independently — exactly the approach I will apply to this AI traineeship.
2Question

Describe a project where you encountered unexpected results and had to troubleshoot your way to a solution.

Situation: During a personal Python project to analyse three months of sales data, my Pandas aggregation was returning figures 22% higher than the source spreadsheet. Task: I needed to identify the discrepancy before presenting findings to a manager. Action: I systematically printed intermediate DataFrame shapes at each transformation step, discovering that a left join was duplicating rows due to non-unique keys in the secondary table. I switched to an inner join and deduplicated on order ID. Result: The corrected output matched the source data exactly. The experience reinforced the importance of validating data at every pipeline stage — a principle central to the data fundamentals module in this programme.

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