AI Engineer
Build real products powered by machine learning models and LLMs.
What does a AI Engineer do?
AI engineers design and build applications that use AI models — from integrating APIs to building entire AI-powered pipelines. You bridge the gap between research and shipping, turning model capabilities into reliable, useful products that people actually use.
Who it fits
Builders who want to use AI to solve real problems, combine software engineering with cutting-edge models, and ship useful tools.
Great for experimental builders who want to combine engineering with the latest AI capabilities.
Skills you need
Tools you will use
Starter projects
Good projects to practise and add to a portfolio.
- Chatbot over your own documents (RAG)
- AI-powered content summariser
- Semantic search over a knowledge base
- LLM-powered task automation tool
// ROADMAP
AI Engineer career roadmap
Estimated time: 10–16 months
Badges show what each resource is. Only items marked certification lead to a professional credential — everything else is learning material.
Python & Software Engineering
6–8 weeksAI engineers write production code. Get comfortable with Python, Git, APIs, and basic software design patterns. These skills matter more than knowing the latest model architecture.
ML Fundamentals & LLM Basics
6–8 weeksLearn how machine learning works conceptually, then focus on how large language models (LLMs) work. You do not need to train models — but you need to understand what they can and can not do.
Build with LLM APIs
4–6 weeksUse OpenAI or Anthropic APIs to build real applications. Learn prompt engineering, function calling, and how to structure conversations. Build something that actually does something useful.
RAG & Vector Databases
4–6 weeksRetrieval-Augmented Generation (RAG) is how you connect LLMs to your own data. Learn vector embeddings, semantic search, and tools like Pinecone or ChromaDB.
MLOps & Production Deployment
4–6 weeksShip AI products that stay reliable. Learn FastAPI for serving models, Docker for containerisation, and monitoring patterns for AI systems in production.
// START LEARNING
AI Engineer learning resources
Videos first — they're the fastest way to get moving — then reading, hands-on practice and courses. Only resources badged professional certification award a formal credential.
Machine Learning with Python and Scikit-Learn
freeCodeCamp · 18 hours
An end-to-end introduction to model training, evaluation and practical machine learning.
Learning resource. This does not award a professional certification.
Kaggle Learn
Kaggle
Practical micro-courses in Python, pandas, SQL, machine learning, data visualisation and AI.
Certificate availability depends on the course provider.
Machine Learning Crash Course
Google for Developers
A practical introduction to ML concepts with interactive visualisations, exercises and production guidance.
Certificate availability depends on the course provider.
Hugging Face LLM Course
Hugging Face
A free course covering transformers, model fine-tuning, datasets, tokenizers and sharing models.
Certificate availability depends on the course provider.
PyTorch Tutorials
PyTorch
Official tutorials from tensor fundamentals through neural networks, computer vision and distributed training.
Learning resource. This does not award a professional certification.
// PRACTISE
Gain AI Engineer work experience
Build practical evidence before your first role. Provider terms and eligibility can change.
Extern · Remote company externships
externship · REMOTE
Work on structured real-company projects with flexible remote delivery.
GitHub · Good first issues
open-source · REMOTE
Find newcomer-labelled issues and build evidence of collaboration in public repositories.
Devpost · Hackathons and build challenges
hackathon · MIXED
Join time-boxed challenges, form teams and ship portfolio-ready prototypes.
// FIND WORK
Find AI Engineer jobs
Start with “AI Engineer”. Platforms without stable public search URLs open with this suggested phrase.
LinkedIn Jobs
Broad professional job search with keyword, location, experience-level and remote filters.
Wellfound
Startup and technology roles with company and compensation context.
Dice
Specialist technology roles across engineering, data, security and infrastructure.
Built In
Technology and startup job discovery, including remote and city-focused listings.
UK visa and international opportunities
Sponsorship depends on the employer, vacancy and current immigration rules. Use the linked official guidance and verify every role before applying.
Going independent
The hottest independent path right now. Businesses desperately need AI integration help and there are not enough engineers to meet demand.
AI Integration Consulting
Low barrierHelp businesses integrate LLMs and AI tools into their workflows and products.
Examples
- LLM-powered customer support automation for SMEs
- Internal knowledge base and document Q&A systems
- Content generation pipelines for media companies
- AI agent workflows for business process automation
Getting started
- 1. Build 2–3 demo AI applications in your niche and share them publicly
- 2. Target companies actively talking about AI on LinkedIn — offer concrete help
- 3. Create a "free AI opportunity audit" as a lead generation service
- 4. Price at £500–£1,000/day — the demand currently supports it
AI-Powered SaaS Products
Medium effortBuild software products powered by AI capabilities that solve specific problems.
Examples
- AI writing tools for specific industries (legal, medical, financial)
- Automated document processing and data extraction
- AI-powered research and summarisation tools
- Niche workflow automation powered by LLMs
Getting started
- 1. Pick a specific profession with an expensive, repetitive task
- 2. Build with an LLM API (Anthropic Claude or OpenAI) as the intelligence layer
- 3. Charge from day one — even a waitlist with a payment validates demand
- 4. Aim for £50–£200/month per user for a B2B tool
Communities & tools
// EARNING POTENTIAL
Earning potential
2025The fastest-growing and highest-paid emerging role in tech. Demand currently far outstrips supply, and this is expected to continue for the foreseeable future.
Freelance rates
Day rate
£400–£1k
Hourly rate
£50–£125
What affects salary
- LLM API expertise (Anthropic, OpenAI) is highly valued and in short supply
- RAG system design and vector database experience command a premium
- Demonstrated shipped AI products matter more than certificates in this field
- ML fundamentals prevent you from being limited to prompt engineering roles
- UK AI companies (DeepMind, Wayve, Stability AI) offer competitive packages
🌍 US AI engineers at major labs and product companies earn $160k–$300k+. UK AI salaries are growing rapidly as the London AI scene expands.
Based on: LinkedIn Salary · Glassdoor UK · Adzuna · Levels.fyi. All figures approximate.
// RESOURCES
Free resources to get started
Recommended starting points — no payment required.
- Course
- Course
- Reading
- Video
- Reading
Paid courses worth considering
These are not required — free resources above can get you far.
- Course
- Course
Browse all resources
Resource directory.
Free and paid resources across every tech career path — searchable and filterable.
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