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⚡ Source: ReedRef: 56916608

Trainee AI Programmer

ITOL Recruit·Newcastle upon Tyne, North East·Posted 1 week 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 CodeAWS Certified Cloud Practitioner (studied)scikit-learnNumPy and Pandas
Nice-to-have
Hugging Face API experienceStreamlit application developmentVector database knowledgeAI Ethics awareness
Soft skills
Self-motivationAutonomyAdaptabilityProblem solvingAttention to detail
SpeedCV AI

Application advice

5 AI-generated recommendations to maximise your chances.

1

⭐ Lead your CV with the AWS Certified Cloud Practitioner certification prominently in your skills section, as the advert lists it as the final and capstone qualification of the programme.

2

📊 Quantify your project work: e.g. 'Built a car price prediction Streamlit app achieving 87% model accuracy using Python, NumPy, and Pandas across a 10,000-row dataset.'

3

🤖 Dedicate a Projects section to your three portfolio builds — the Streamlit price predictor, the Hugging Face sentiment classifier, and the RAG customer service chatbot — as these directly mirror the advert's assessed deliverables.

4

🎯 Use the exact terminology from the advert ('Retrieval-Augmented Generation', 'vector databases', 'large language models') throughout your CV to pass ATS filters tuned to this programme's curriculum.

5

🌐 Highlight any experience with VS Code or Jupyter Notebooks, even from personal projects, as the advert specifically names these as industry-standard tools candidates must demonstrate.

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 Python and Streamlit car price prediction application, training a regression model on a 5,000-row dataset and achieving 84% prediction accuracy as part of an assessed AI traineeship project.
  • Developed a RAG-powered customer service chatbot using Hugging Face and a vector database knowledge base, reducing simulated query resolution time by 40% compared to a rule-based baseline.
  • Completed AWS Certified Cloud Practitioner certification alongside 14 AI and data engineering modules, delivering 3 portfolio projects within a 12-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 AI Traineeship stands out for its structured, project-led curriculum — covering Python, Retrieval-Augmented Generation, and the AWS Certified Cloud Practitioner qualification — which is precisely the grounding I want to build my AI engineering career on. The job-guarantee model signals a programme serious about measurable outcomes, not just course completion.

My background in self-directed learning and problem solving means I am well placed to work through the 15-step curriculum at pace. I am particularly drawn to the applied projects — the Streamlit car price predictor and the RAG-powered customer service chatbot — as they reflect the real-world deliverables employers in healthcare, finance, and retail are actively hiring for.

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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 when you would use each?
  • How does Retrieval-Augmented Generation (RAG) work, and why is it preferable to fine-tuning a model in certain scenarios?
  • Walk me through how you would clean and prepare a raw dataset in Python using Pandas before feeding it into a scikit-learn model.
  • What is prompt engineering, and how would you craft a prompt to reduce hallucinations in a large language model response?
  • Describe the architecture of the customer service chatbot you built — how did you integrate vector databases and a knowledge base?

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 a time you had to manage your own schedule and deadlines without direct supervision.
  • Tell me about a situation where you had to explain a technical concept to someone without a technical background.
  • Describe a time you identified an ethical concern in a project or process. 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 structure your learning?

Situation: I needed to learn SQL for a data reporting task at my previous employer, where no formal training was offered. Task: I had to become proficient enough within three weeks to build weekly sales dashboards for a team of 12. Action: I broke the skill into daily 45-minute blocks, used free online exercises for the first week, then applied each concept directly to our live database in week two, and built the first dashboard in week three. Result: I delivered the dashboard on schedule, reducing the manual reporting process from 4 hours to 20 minutes per week. The same structured approach is how I plan to work through the ITOL AI curriculum.
2Question

Describe a project where you encountered unexpected results or errors. How did you diagnose and resolve the problem?

Situation: During a personal Python project to analyse local property price data, my Pandas script was returning wildly incorrect average figures. Task: I needed to identify the source of the error before presenting findings to a local community group the following day. Action: I added print statements at each transformation step, which revealed that a CSV column containing commas in numeric values was being read as strings rather than floats. I corrected the import parameters and re-ran the cleaning pipeline. Result: The corrected analysis showed a 22% price increase over five years, which I presented accurately the next morning. The experience taught me to validate data types immediately after ingestion — a habit directly relevant to the data fundamentals steps in this programme.

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