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⚡ Source: ReedRef: 56950086

Junior AI Developer

ITOL Recruit·Derby, East Midlands·Posted 6 days ago
💰 £30-45k/year🌱 Junior
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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 Practitionerscikit-learnHugging FaceStreamlit
Nice-to-have
Vector database integrationObject-oriented programming (OOP)Data visualisation with MatplotlibAI data ethics knowledge
Soft skills
Self-motivationAutonomyAdaptabilityProblem solvingAttention to detail
SpeedCV AI

Application advice

5 AI-generated recommendations to maximise your chances.

1

⭐ Highlight any self-directed learning projects at the top of your CV — the advert values candidates who can train online at their own pace, so demonstrating initiative in personal projects signals exactly that.

2

📊 Quantify your project outcomes: e.g. 'Built a car price prediction Streamlit app achieving 87% model accuracy on a 10,000-row dataset' to mirror the hands-on project structure of the programme.

3

🌐 List your AWS Certified Cloud Practitioner certification prominently in a dedicated Certifications section, as it is the final and most externally recognised credential from this programme.

4

🎯 Reference specific tools from the advert (Jupyter Notebooks, VS Code, Hugging Face, scikit-learn) in a Technical Skills section — ATS systems will scan for these exact names.

5

🤝 Include a brief Personal Statement noting your transition into AI engineering, referencing the RAG and prompt engineering modules, as these are among the most in-demand specialisms cited in the advert.

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:

  • Built a car price prediction web application using Python and Streamlit, processing a 5,000-row dataset and achieving a mean absolute error 12% below baseline using scikit-learn regression models.
  • Developed an AI-powered customer service chatbot integrating RAG techniques and a vector database, reducing average query resolution time by 30% in a simulated 500-query test environment.
  • Completed AWS Certified Cloud Practitioner certification alongside 14 AI engineering modules, delivering 3 end-to-end projects in Python covering data preprocessing, sentiment analysis, and machine learning within a 10-week self-study 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 AI Traineeship is precisely the structured entry point I have been seeking to launch a career in AI engineering. The programme's focus on Python, prompt engineering, and Retrieval-Augmented Generation aligns directly with the skills I am committed to building, and the AWS Certified Cloud Practitioner qualification at the end adds a credential that carries real weight with employers across every sector.

My background in self-directed learning and problem solving means I am well placed to progress through the 15-module curriculum at pace. I am particularly drawn to the hands-on project work — building a sentiment analysis classifier with Hugging Face and an AI-powered customer service chatbot using RAG and vector databases are exactly the kind of portfolio pieces that demonstrate applied competence to future employers.

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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 use case for each?
  • How does Retrieval-Augmented Generation (RAG) differ from a standard large language model response, and when would you use it?
  • Walk me through how you would clean and prepare a raw dataset using Pandas before feeding it into a scikit-learn model.
  • What is prompt engineering and how would you craft an effective prompt to control the output of an AI model?
  • Describe the architecture of the customer service chatbot project: how do vector databases and knowledge bases interact with the LLM?

Behavioural

  • Tell me about a time you had to learn a new technical skill independently under time pressure. How did you approach it?
  • Describe a situation where you identified an ethical concern in a project or process. What did you do?
  • Give an example of a project where you had to work with incomplete or messy data. How did you handle it?
  • Tell me about a time you had to explain a technical concept to a non-technical audience. What was your approach?
  • Describe a situation where you set yourself a challenging goal and had to stay motivated to complete it without external 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 new technical skill independently under time pressure. How did you approach it?

Situation: In a previous administrative role, our team was asked to migrate reporting to a new data tool with no formal training provided and a two-week deadline. Task: I needed to become proficient enough to rebuild 8 weekly reports from scratch. Action: I dedicated 90 minutes each evening to structured online tutorials, created a personal cheat sheet of key functions, and tested each report against the legacy outputs to validate accuracy. I also documented my process so colleagues could follow it. Result: All 8 reports were delivered on time, with zero discrepancies flagged by the finance team, and my documentation was adopted as the team's onboarding guide for the new tool.
2Question

Describe a situation where you set yourself a challenging goal and had to stay motivated to complete it without external supervision.

Situation: After deciding to change careers, I committed to completing a 12-module online data analysis course while working full-time, with no manager or tutor checking my progress. Task: I needed to finish within 10 weeks to align with a job application deadline. Action: I broke the curriculum into weekly targets, blocked out 6 hours every weekend, and used a progress tracker to stay accountable. When I hit a difficult section on statistical modelling, I sought out supplementary YouTube resources rather than skipping ahead. Result: I completed the course in 9 weeks, scored 91% on the final assessment, and used the certificate to secure an interview within a fortnight of applying.

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