AI & Generative AI · AI Engineer
Build, Evaluate and Run AI Applications
Go from the fundamentals of machine learning and large language models to building retrieval-augmented AI applications, and learn how to evaluate, monitor and deploy them responsibly.
- Machine-learning and LLM fundamentals
- Embeddings and vector search
- Retrieval-augmented generation (RAG)
- Evaluation, monitoring and responsible AI
Course overview
About This Course
AI Engineer covers the skills needed to build practical applications on top of modern AI models, from how models learn to how AI features are evaluated and kept reliable in production.
Organisations are adding AI to search, support, analytics and internal tools. Building these features well takes more than calling a model: engineers need to understand model behaviour, retrieve the right data, measure quality and manage risk.
You will learn the fundamentals of machine learning and large language models, how embeddings and vector search work, how to build retrieval-augmented generation (RAG) applications, and how to evaluate, monitor and deploy AI features responsibly.
This is the broadest of our AI & Generative AI learning paths. Generative AI Development goes deeper into LLM integration, AI Agents & Automation covers multi-step workflows, and AI Security covers how to protect AI applications.
Who it is for
Who Should Take This Course?
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Software developers
You build applications and want to add AI features with confidence.
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Data professionals
You work with data and want to move into building AI applications.
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Technologists exploring AI
You want a structured understanding of modern AI beyond the headlines.
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Technical leads
You need to understand how AI systems are built, evaluated and run.
Learning outcomes
What You Will Learn
By working through the course, you will build towards being able to:
Explain core machine-learning ideas such as training, features, overfitting and evaluation.
Describe how large language models generate text, and their strengths and limitations.
Create embeddings and use vector search to find relevant information.
Build a retrieval-augmented generation application over a document collection.
Integrate AI models into applications through APIs.
Evaluate AI output quality with test sets and measurable criteria.
Apply responsible AI practices, including bias awareness, transparency and monitoring.
Curriculum
Course Curriculum
The course is organised into modules. Session order and depth may vary by training arrangement.
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Module 1: AI and machine-learning fundamentals
The ideas behind how models learn from data.
Topics covered include
Practical activity: Train and evaluate a simple model on a sample dataset.
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Module 2: Large language models
How modern language models work and what they can and cannot do.
Topics covered include
Practical activity: Compare model responses to the same task and explain the differences.
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Module 3: Embeddings and vector search
Represent meaning as numbers and search by similarity.
Topics covered include
Practical activity: Embed a set of sample documents and build a semantic search over them.
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Module 4: Retrieval-augmented generation
Ground model answers in your own data.
Topics covered include
Practical activity: Build a question-answering assistant over a sample document collection.
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Module 5: AI application development
Turn AI prototypes into usable application features.
Topics covered include
Practical activity: Wrap the assistant in a simple application with error handling.
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Module 6: Evaluation, monitoring and responsible AI
Measure quality and run AI features responsibly over time.
Topics covered include
Practical activity: Create an evaluation set for the assistant and measure improvements.
Practical learning
Hands-On Learning
Practical work builds an AI application step by step. Examples of activities include:
Some activities use AI model APIs. Whether accounts or API access are provided, or you use your own, will be explained when you enquire.
- Training and evaluating a simple model
- Building semantic search with embeddings
- Creating a retrieval-augmented assistant
- Evaluating and improving AI output quality
Before you start
Prerequisites and Learning Requirements
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Recommended background
Programming experience, ideally in Python, is recommended.
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Technical prerequisites
Basic understanding of APIs and data formats such as JSON. Some familiarity with statistics is helpful but not required.
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Software and tools
A computer with a reliable internet connection for live online sessions. Python and a code editor are used for practice. Any additional software or accounts for practical activities will be confirmed when you enquire.
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Prior experience
Not intended as a first programming course.
Learning Format
IT Champion Hub offers live, instructor-led online training and workshops. Formats, duration and schedules vary by course and are not the same for every program.
- Live online sessions led by an instructor
- Practical activities alongside explanations
- Upcoming batches and schedules shared on enquiry
Enquire to find out the formats and upcoming batches available for this course. Enquire about this course
FAQ
Frequently Asked Questions
Do I need a machine-learning background?
No. The course introduces machine-learning fundamentals before moving to language models and applications. Programming experience is recommended.
Which programming language is used?
Python, the most widely used language for AI development.
Will I need access to AI model APIs?
Some practical activities use AI model APIs. We'll explain how access is arranged, and any costs involved, when you enquire.
How is this different from Generative AI Development?
AI Engineer is broader, covering machine-learning fundamentals, RAG, evaluation and responsible AI. Generative AI Development focuses on integrating LLMs into applications.
How can I enquire about upcoming training?
Select Enquire About This Course. The enquiry form will note that you are asking about AI Engineer, and we'll get back to you with upcoming batches and available schedules.
Ready to Explore This Learning Path?
Have questions about the curriculum, prerequisites, or training arrangements? Get in touch with IT Champion Hub to discuss your learning goals.