2. What skills will I gain from this AI Engineering Course?

You will learn how to work with AI model APIs, integrate language models, build AI application features, manage conversation context, develop RAG applications, connect knowledge bases, and create AI agents. You will also learn AI evaluation, optimisation, security, monitoring, and deployment techniques.

3. Can AI engineering skills support a technology career?

AI engineering skills can complement careers in software development, application engineering, machine learning, data technology, and AI development. The course provides knowledge for building and deploying applications that integrate modern AI models, agents, APIs, and knowledge systems.

4. Do I need programming experience for this AI Engineering Course?

Yes, basic programming or software development knowledge is recommended for this course. Learners will work with APIs, application configuration, data, AI integrations, testing, security, and deployment, so familiarity with programming concepts can support successful learning.

5. What projects are included in the AI Engineering Course?

The course includes projects involving an AI assistant, knowledge-based application, AI agent, external API integrations, application testing, and deployment. These projects allow learners to apply AI engineering concepts across the development lifecycle.

AI Engineering Course: Build AI-Powered Applications

Category Data Analytics & AI

Level Experts

Students 13 Already Enrolled

Review

(4.7)

The AI Engineering Course teaches learners how to design, develop, evaluate, secure, and deploy AI-powered applications using modern artificial intelligence technologies. It provides a structured foundation in AI application architecture, model APIs, development workflows, data handling, language models, and intelligent application features.

Learners explore AI application development, structured and unstructured data, conversational context, retrieval-augmented generation, document pipelines, vector search, knowledge bases, AI agents, external tools, APIs, memory, and multi-step agentic workflows. The course also covers evaluation metrics, prompt quality, output testing, hallucination reduction, and application performance.

Security and deployment are integrated throughout the training, including API key protection, prompt injection prevention, access controls, monitoring, and deployment. Through practical projects, learners build AI assistants, knowledge-based applications, AI agents, API integrations, and deployable AI-powered solutions.

AI engineering foundations, architecture, models, and APIs
AI application development and language model integration
Retrieval-augmented generation, knowledge bases, and vector search
AI agents, tools, APIs, memory, and agentic workflows
AI application evaluation, testing, optimisation, and performance
Security, governance, monitoring, deployment, and AI engineering projects

Basic programming or software development knowledge is recommended because the course involves APIs, application development, data handling, and deployment. Learners should have basic computer skills and an interest in artificial intelligence and software engineering. Prior professional AI engineering experience is not required.

This course is suitable for software developers, aspiring AI engineers, programmers, technology professionals, application developers, and technical entrepreneurs interested in building AI-powered applications. It is also useful for learners who want to develop skills in language models, RAG systems, AI agents, APIs, evaluation, and AI application deployment.

1.1 Understanding AI Engineering
1.2 AI-Powered Application Architecture
1.3 Machine Learning and Generative AI
1.4 AI Models and APIs
1.5 AI Engineering Development Workflow
1.6 AI System Capabilities and Limitations

2.1 Setting Up the Development Environment
2.2 Working with AI Model APIs
2.3 Managing API Requests and Responses
2.4 Handling Structured and Unstructured Data
2.5 Building Basic AI Features
2.6 Managing Application Configuration

3.1 Designing AI Application Architecture
3.2 Integrating Language Models
3.3 Building AI-Powered User Interfaces
3.4 Creating AI-Assisted Workflows
3.5 Managing Conversation Context
3.6 Handling Application Errors

4.1 Understanding Retrieval-Augmented Generation
4.2 Preparing Knowledge Sources
4.3 Creating Document Processing Pipelines
4.4 Building Vector Search Systems
4.5 Connecting Knowledge Bases
4.6 Improving Retrieval Quality

5.1 Understanding AI Agent Architecture
5.2 Designing Agentic Workflows
5.3 Connecting External Tools
5.4 Integrating APIs and Services
5.5 Managing Agent Memory
5.6 Building Multi-Step AI Agents

6.1 Defining AI Evaluation Metrics
6.2 Testing AI Application Outputs
6.3 Managing Prompt Quality
6.4 Improving Model Responses
6.5 Reducing AI Hallucinations
6.6 Optimizing Application Performance

7.1 Protecting AI Application Data
7.2 Managing API Keys and Secrets
7.3 Preventing Prompt Injection
7.4 Implementing Access Controls
7.5 Monitoring AI Applications
7.6 Deploying AI Applications

8.1 Building an AI Assistant
8.2 Creating a Knowledge-Based Application
8.3 Developing an AI Agent
8.4 Integrating External APIs
8.5 Testing and Refining Applications
8.6 Deploying an AI-Powered Solution

The course is designed for developers, programmers, aspiring AI engineers, technology professionals, and learners interested in building AI-powered applications. Basic programming knowledge is recommended because the curriculum includes APIs, application development, data handling, and deployment.

AI Engineering Course: Build AI-Powered Applications

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