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⚡ Source: ReedRef: 56994341

Trainee AI Engineer

ITOL Recruit·Gloucester, Gloucestershire·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 NotebooksAWS Certified Cloud Practitioner (course completion)Machine Learning fundamentalsData preparation and cleaning
Nice-to-have
Prior exposure to scikit-learnFamiliarity with Hugging Face modelsExperience with vector databasesBasic cloud computing knowledge
Soft skills
Self-motivationAutonomyAdaptabilityProblem solvingAttention to detail
SpeedCV AI

Application advice

5 AI-generated recommendations to maximise your chances.

1

⭐ Highlight any Python or data projects at the top of your CV — the advert lists Python across four dedicated steps, signalling it is the core language for this role.

2

📊 Quantify portfolio projects: e.g. 'Built a sentiment analysis classifier using Hugging Face, achieving 91% accuracy on a 10,000-record dataset' to stand out against other trainees.

3

🎯 Include the AWS Certified Cloud Practitioner certification prominently once earned — the advert names it as the final capstone and employers will scan for it as proof of programme completion.

4

🤝 Reference AI ethics knowledge explicitly on your CV, as the programme dedicates a full module to bias, fairness, and data privacy — increasingly required by regulated-sector employers in healthcare and finance.

5

🌐 Showcase your RAG chatbot project in a GitHub portfolio linked from your CV, as the advert's Step 10 project (customer service chatbot with vector databases) is a directly deployable, employer-ready artefact.

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 Python Streamlit car price prediction application as part of a structured AI engineering curriculum, demonstrating end-to-end model deployment within a 4-week project sprint.
  • Built a RAG-powered customer service chatbot integrating Hugging Face embeddings and a vector database knowledge base, reducing simulated query resolution time by 40% versus a baseline keyword search.
  • Completed AWS Certified Cloud Practitioner certification alongside 14 AI and machine learning modules, applying scikit-learn to train and evaluate classification models across 3 hands-on projects.

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-driven approach — covering Python, Retrieval-Augmented Generation, and the AWS Certified Cloud Practitioner qualification — which aligns precisely with the direction I want my career to take. The job-guarantee model and the breadth of the 15-step curriculum make this the most credible entry point into AI engineering I have encountered.

My background in self-directed learning and problem solving has equipped me with the discipline required to complete an intensive online programme at pace. I am confident I can apply the Python, machine learning, and prompt engineering skills taught in the course to real-world employer challenges from day one, particularly in building RAG-powered applications and data pipelines.

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SpeedCV AI

Interview questions

10 questions generated from this advert.

Technical

  • Can you explain the difference between a large language model and a traditional machine learning model, and when you would choose each?
  • Walk me through how Retrieval-Augmented Generation works and describe a use case where it outperforms a fine-tuned model.
  • How would you prepare a raw dataset for a machine learning pipeline using Python's Pandas and NumPy libraries?
  • What is prompt engineering, and how would you craft a prompt to reduce hallucinations in an LLM-based customer service chatbot?
  • Describe the role of vector databases in an AI system and explain how you would index and query embeddings for a knowledge base.

Behavioural

  • Tell me about a time you had to learn a complex technical skill independently and how you structured your approach.
  • Describe a situation where you identified an ethical concern in a project or process and how you handled it.
  • Give an example of a time you built something from scratch with limited guidance — what was the outcome?
  • Tell me about a time you had to troubleshoot a problem that had no obvious solution. What steps did you take?
  • Describe a situation where you had to manage your own time and priorities to meet a deadline without direct supervision.
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 complex technical skill independently and how you structured your approach.

Situation: I needed to learn SQL for a data reporting project at my previous employer, with no formal training budget available. Task: I had to become proficient enough to build weekly sales dashboards within three weeks. Action: I structured my learning into daily 90-minute sessions using free online resources, built five practice queries each day against a sample dataset, and documented my progress in a learning log. I also joined an online community forum where I could ask questions and review others' code. Result: I delivered the first dashboard on schedule, reducing the finance team's manual reporting time by six hours per week. The structured self-study approach I developed then is exactly how I plan to tackle the ITOL programme.
2Question

Describe a situation where you identified an ethical concern in a project or process and how you handled it.

Situation: While working on a customer segmentation project at a retail company, I noticed the dataset used to group customers was missing records from lower-income postcodes, skewing promotional targeting. Task: I needed to flag this without derailing the project timeline. Action: I documented the gap with specific figures — 18% of postcodes were unrepresented — and presented two options to the project lead: delay by one week to source supplementary data, or proceed with a clearly labelled limitation caveat. I also drafted a short bias-risk note for the stakeholder report. Result: The team chose the one-week delay, the final model covered 97% of postcodes, and the stakeholder report included a data-quality section that became standard practice for subsequent projects.

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