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⚡ Source: ReedRef: 56994342

Trainee AI Engineer

ITOL Recruit·Burnley, Lancashire·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 Codescikit-learnAWS Certified Cloud Practitioner
Nice-to-have
Hugging Face model integrationStreamlit application developmentVector database integrationPrompt engineering
Soft skills
Self-motivationAutonomyAdaptabilityAttention to detailProblem solving
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 or skills section, as the advert lists it as the final capstone credential employers will look for.

2

📊 Quantify your training projects: e.g. 'Built a sentiment analysis classifier using Hugging Face achieving 91% accuracy on a 10,000-record dataset' to demonstrate tangible output from the programme.

3

🌐 Showcase your RAG chatbot project in a GitHub portfolio and link it on your CV — the advert specifically names vector databases and knowledge bases as deliverables that hiring managers will want to see.

4

🎯 Structure your CV with a 'Projects' section listing all 5 hands-on builds (car price prediction app, sentiment classifier, customer service chatbot, ML project, Streamlit app) since you will have limited work history to reference.

5

🤝 Reference AI ethics knowledge explicitly in your personal statement, as the advert dedicates a full module to bias, fairness, and data privacy — a differentiator few entry-level candidates mention.

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 RAG-powered customer service chatbot integrating Hugging Face LLMs with a vector database knowledge base, reducing simulated query resolution time by 40% versus a rule-based baseline.
  • Built and trained a car price prediction model using Python and Streamlit, processing a 5,000-row dataset with NumPy and Pandas to achieve a mean absolute error of under £800.
  • Completed AWS Certified Cloud Practitioner examination alongside a 15-module AI engineering curriculum, delivering 5 end-to-end portfolio projects within a 3-month self-directed 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-driven curriculum — particularly the Retrieval-Augmented Generation module and the AWS Certified Cloud Practitioner certification — which align precisely with the skills employers are actively seeking. Having completed a programme that spans Python programming, machine learning with scikit-learn, and AI ethics, I am confident I can contribute meaningfully from day one.

My background in self-directed learning and delivering hands-on projects — including a sentiment analysis classifier built with Hugging Face and a RAG-powered customer service chatbot using vector databases — demonstrates my ability to apply technical knowledge to real-world problems. I thrive when working autonomously and am comfortable navigating complex data workflows from collection through to model deployment.

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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

  • Can you explain the difference between supervised and unsupervised machine learning, and give an example of each from your training projects?
  • Walk me through how Retrieval-Augmented Generation works and how you implemented it in your customer service chatbot project.
  • What Python libraries did you use for data manipulation during your training, and what are the key differences between NumPy and Pandas?
  • How does the AWS Certified Cloud Practitioner certification relate to deploying AI models in production environments?
  • Describe how you would craft an effective prompt for a large language model to minimise hallucinations and improve output accuracy.

Behavioural

  • Tell me about a time you had to learn a completely new technical skill independently — how did you structure your learning?
  • Describe a situation where you encountered a problem you couldn't immediately solve. What steps did you take to work through it?
  • Give an example of a project you completed under your own initiative without direct supervision.
  • How have you managed your time when balancing multiple learning objectives or deadlines simultaneously?
  • Tell me about a time you had to adapt quickly to a change in direction or new information mid-project.
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 structure your learning?

Situation: I decided to transition into AI engineering with no prior coding background, enrolling in a structured online programme while working part-time. Task: I needed to master Python from scratch and deliver a functional machine learning project within 8 weeks. Action: I broke the curriculum into daily 2-hour blocks, prioritised the Python fundamentals modules first, and used Jupyter Notebooks to practice each concept immediately after studying it. I built a car price prediction app using Streamlit to consolidate my learning. Result: I completed the Python and data modules 5 days ahead of schedule and produced a working Streamlit application that processed a 5,000-row dataset with under 4% prediction error.
2Question

Describe a situation where you encountered a problem you couldn't immediately solve. What steps did you take to work through it?

Situation: While building a RAG-powered chatbot during my AI traineeship, the vector database retrieval was returning irrelevant chunks, causing the LLM to produce inaccurate responses. Task: I needed to diagnose and fix the retrieval pipeline within 3 days to meet my project submission deadline. Action: I systematically tested each component — first checking chunk size parameters, then re-examining my embedding model choice, and finally reviewing the similarity threshold settings. I consulted Hugging Face documentation and adjusted the chunk overlap from 20 to 50 tokens. Result: Retrieval accuracy improved by roughly 35%, and the chatbot correctly answered 9 out of 10 test queries compared to 6 out of 10 before the fix.

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