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Complete AI For Software Engineers Course
Hot & New
New
Rating: 4.6 out of 5(23 ratings)
152 students

Complete AI For Software Engineers Course

Master AI & Agentic Engineering from LLMs and RAG to Agents, MCP, Production Systems and AI-Native Architecture
Created byIfeoluwa Mobolaji Olawoye
Last updated 9/2026
English
GermanEnglish [Auto],

What you'll learn

  • Build production-ready AI apps with LLMs, RAG, vector search, tool calling, MCP servers, and AI agents—from prototype to deployment.
  • Design and build autonomous AI agents with memory, tool use, MCP, multi-agent workflows, and reliable orchestration for real business problems.
  • Master RAG and context engineering: embeddings, vector databases, hybrid search, reranking, evaluation, and grounded AI knowledge assistants.
  • Design production AI systems with security, guardrails, observability, scaling, cost optimization, evaluation, and human-in-the-loop controls.
  • Go beyond chatbots with multimodal AI, fine-tuning, LoRA/QLoRA, RLHF, AI alignment, interpretability, and responsible AI concepts.
  • Turn software engineering skills into AI engineering opportunities with portfolio projects, AI SaaS ideas, consulting paths, and a practical career roadmap.

Course content

10 sections • 65 lectures • 13h 45m total length

  • Why This Shift Matters – The Three Patterns That Power AI10:03

    Lesson Objective

    This lesson establishes the urgency and reality of AI transformation by exploring how AI represents a fundamental platform shift (like web and cloud before it), demonstrating what AI can do today (generate functions, debug, analyze codebases), and introducing the three core architectural patterns—chat interfaces, retrieval systems, and autonomous agents—that power all modern AI systems. By the end of this lesson, you will understand that this is a real transformation (not hype), see the three patterns that define every AI system you'll build, and recognize why your foundational systems thinking skills are MORE valuable than ever.


    Keywords

    • Platform shift

    • Three core patterns

    • Chat interfaces

    • Retrieval-Augmented Generation

    • Autonomous agents

    • AI transformation

    • Systems thinking

    • Skill relevance


    Business Scenario:

    N/A


    Type: Talking Head

    Repository Folder: N/A

    Code File: N/A

  • Why This Matters for You – Mindset, Leverage & Your Path Forward9:10

    Lesson Objective

    This lesson translates the three patterns from Lesson 1.1 into concrete implications for your career and thinking, addressing the shift from code executor to system designer, clarifying five essential mindset changes, and showing the leverage you gain from understanding AI patterns while building on your existing engineering foundation. By the end of this lesson, you will understand how your role is evolving, have internalized the mindset shifts required for AI-first thinking, know the leverage you're gaining (not losing), and be ready to commit to this transition.

    Keywords

    • Career implications

    • Mindset shifts

    • System design

    • Leverage

    • Role evolution

    • Uncertainty design

    • Strategic thinking

    • AI-first architecture

    Business Scenario:

    N/A

    Type: Talking Head

    Repository Folder: N/A

    Code File: N/A

  • Your Skills Are Safe17:20

    Lesson Objective

    This lesson directly addresses the three most common anxieties experienced engineers face—obsolescence, skill devaluation, and being left behind—by examining each fear honestly and showing that your 10+ years of experience is a foundation, not a liability, and that you're positioned for advantage precisely because you understand systems deeply. By the end of this lesson, you will understand that your role is being elevated (not automated), that your foundational skills are more valuable than ever, and that starting "late" in AI is actually starting at exactly the right time for your experience to matter most.


    Keywords

    • Career relevance

    • Skills transferability

    • Obsolescence vs elevation

    • Durable skills

    • Systems thinking

    • Debugging expertise


    Business Scenario:

    N/A


    Type: Talking Head

    Repository Folder: N/A

    Code File: N/A

  • Your Complete Learning Path12:32

    Lesson Objective

    This lesson provides a comprehensive overview of the 10-module learning architecture, showing you not just what you'll learn but what you'll be able to build at each stage, how modules build on each other, and how your personal project from Lesson 1.5 will be supported by each subsequent module. By the end of this lesson, you will understand the complete pathway from foundational concepts through production deployment, recognize how each module serves your learning goals, and feel confident about the entire journey ahead.


    Keywords

    • Module architecture

    • Learning dependencies

    • AI fundamentals

    • Production systems

    • Career outcomes

    • Skill progression


    Business Scenario:

    N/A


    Type: Talking Head + Animated Graphics

    Repository Folder: N/A

    Code File: N/A

  • Module 1 Assessment
  • Module 1 Appendix & Resources4:26

    Explore the research, industry insights, technical resources, and further reading that support Module 1’s discussion of AI’s transformation of software engineering, LLM fundamentals, RAG, AI agents, developer productivity, and the evolving role of software engineers.

Requirements

  • Ideally, you should have atleast 1-2 years experience as a programmer/developer. Basic knowledge of Python would be beneficial although not required

Description

AI is changing software engineering. This course shows you how to change with it.

AI is not simply another technology to add to your developer toolkit. It is changing how software is designed, built, deployed, and operated.

Complete AI For Software Engineers Course is a practical, engineering-focused course designed to help software engineers transition from traditional software development into modern AI engineering.

You will not just learn how AI works. You will learn how to build with AI, design AI-native systems, integrate AI into real applications, operate AI systems in production, and turn your AI engineering skills into career and business opportunities.

What You Will Learn

The course takes you through a structured progression from AI fundamentals to production-grade AI engineering.

Module 1 – Foundations & Mindset

Understand why AI represents a fundamental platform shift and how software engineering skills transfer into the AI era. You will explore the three core patterns behind modern AI systems: chat interfaces, retrieval systems, and autonomous agents.

Module 2 – AI Fundamentals

Build a practical understanding of Large Language Models, tokens, context windows, transformers, embeddings, vector search, RAG, and the technical foundations behind modern AI systems.

Module 3 – AI Developer Toolkit

Move from theory into implementation. Learn how to work with LLM APIs, rapidly prototype AI applications, build chat interfaces, apply AI-focused development practices, and turn LLM capabilities into usable software features.

Module 4 – Practical RAG & Context Engineering

Learn how modern AI applications work with proprietary knowledge. Build retrieval-augmented generation systems and explore document ingestion, embeddings, vector databases, retrieval strategies, context engineering, evaluation, and enterprise knowledge assistants.

Module 5 – Developing MCP Servers & Tooling

Learn how AI systems interact with external tools and capabilities. Build MCP servers and tools that allow AI applications and agents to interact with real-world systems and perform useful operations.

Module 6 – AI Agents & Autonomy

Move beyond simple prompt-response applications and learn how autonomous AI systems plan, reason, use tools, manage state, execute workflows, and operate with increasing levels of autonomy.

Module 7 – Designing AI-Native Systems

Learn how to architect systems where AI is a fundamental part of the product rather than an isolated feature. Explore AI-native architecture, context engineering, memory, orchestration, human-in-the-loop design, and system-level AI patterns.

Module 8 – Production AI Systems

Learn what it takes to move AI applications from prototypes into production. Explore reliability, observability, evaluation, security, cost management, deployment, monitoring, and operational practices for production AI systems.

Module 9 – Advanced Capabilities & Specializations

Explore advanced AI engineering capabilities and specializations that build on the foundations of the previous modules, preparing you for increasingly sophisticated AI engineering, architecture, platform, and technical leadership responsibilities.

Module 10 – Career Transition & Monetization

Turn your technical capabilities into professional opportunities. Explore AI engineering career paths, portfolio development, AI SaaS opportunities, consulting and freelancing, research, and continuous learning.

This Course Is Different

This is not a course about simply learning how to write better prompts.

It is designed around the way software engineers actually need to think about AI:

Understand → Build → Integrate → Architect → Deploy → Operate → Evolve

You will progressively move from understanding AI concepts to building working AI applications and ultimately thinking at the level required to design and operate AI-native systems.

The course also connects technical implementation with business value. Throughout the curriculum, AI concepts are framed around realistic engineering and business scenarios so that you understand not only how something works, but why and when you would use it.

By the End of This Course

You will have developed a practical foundation across:

  • Large Language Models and AI fundamentals

  • LLM APIs and AI application development

  • Prompt and context engineering

  • Embeddings and vector search

  • Retrieval-Augmented Generation (RAG)

  • MCP servers and AI tooling

  • AI agents and autonomous workflows

  • AI-native system architecture

  • Production AI engineering

  • AI evaluation and reliability

  • AI operations and deployment

  • Advanced AI engineering capabilities

  • AI engineering career development

  • AI consulting, freelancing, and monetization

More importantly, you will have a framework for continuously adapting as AI evolves.

Who This Course Is For

This course is primarily designed for:

  • Software engineers transitioning into AI engineering

  • Full-stack developers who want to build AI-powered applications

  • Backend engineers working with LLMs and AI services

  • Developers who want to understand RAG, MCP, and AI agents

  • Technical professionals moving toward AI architecture

  • Engineers preparing for Senior AI Engineer, AI Platform Engineer, or AI Architect responsibilities

  • Developers interested in building AI products or SaaS businesses

  • Software engineers who want to remain relevant as AI transforms the software industry

You do not need to become a machine-learning researcher to benefit from this course.

The focus is on the engineering knowledge required to build, integrate, architect, deploy, and operate modern AI systems.

Your AI Engineering Journey Starts Here

AI is creating a new generation of software systems—and a new generation of engineering opportunities.

The goal of this course is not simply to teach you today's AI tools.

It is to give you the engineering foundations, architectural thinking, practical experience, and learning framework needed to build with AI today and continue evolving with the technology tomorrow.

Who this course is for:

  • Developers who wish to upgrade to AI and Agentic Engineering
  • Existing developers that worry about the impact of AI on their skills, career and future
  • Existing software engineers & developers who wish to learn powerful AI augmentation tools and methods.
  • Programmers that already have the basic coding knowledge I intend to build upon who will appreciate the 10x power that AI brings to your existing skillset

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