For .NET developers with 1–5+ years of experience
From .NET Developer to AI Engineer
Keep C#, ASP.NET Core and SQL Server. Add LLMs, RAG, AI agents and MCP on top, in a structured 110–120 hour course built for working developers.
No Python requiredNo maths background100% C# & .NET
“I’m a .NET developer. How do I enter the AI world?”
You can see Machine Learning, LLMs, Transformers and Agentic AI becoming part of every product. Your problem is not interest. It is not knowing where to start. These are the questions we hear most.
Do I need to learn Python?
No. The whole course is taught in C# and ASP.NET Core. You use ML.NET, ONNX, Semantic Kernel, the Microsoft Agent Framework and the OpenAI, Gemini and Ollama APIs from .NET.
Do I need a mathematics background?
No. You learn what regression, neural networks and attention do and when to use them, with working code. The focus is on building applications, not deriving equations.
Should I learn Machine Learning before Generative AI?
Yes, but only the part you need. The course starts with ML fundamentals and ML.NET so that LLMs, embeddings and RAG make sense later. It also shows where classical ML stops being enough.
How can I use my existing C# and .NET skills?
They do most of the work. Dependency injection, middleware, EF Core, Web APIs and logging are exactly how production AI services are built. You add the AI layer instead of starting over.
Where should I start?
With the roadmap. Check the prerequisites below, then follow ten modules in a fixed order: AI and ML basics, deep learning, NLP, LLMs, RAG, agents, enterprise AI, a capstone and career preparation. Module 1 even includes a modern .NET refresher, so there are no gaps and no guessing what to learn next.
AI won’t replace developers. Developers with AI skills will replace those without.
Same platform. A bigger future.
You don’t change your stack or restart your career. You extend it.
What you keep
- C# and modern .NET
- ASP.NET Core and Web API
- EF Core and SQL Server
- Dependency injection, middleware, logging
- Git and GitHub
What you add
- ML.NET, ONNX and neural network basics
- LLMs through OpenAI, Gemini and local Ollama
- Embeddings, Qdrant and RAG
- Tool calling, MCP and AI agents
- Security, evaluation and deployment
Module 0: what you need before you join
This is a course for working .NET developers, so the entry bar is simple. There is no Python, no maths exam and no AI experience on the list.
Required knowledge
- .NET Core 10
- ASP.NET Core 10
Required tools
- Visual Studio 2026
Required mindset
- Dedication
- Time to code along with the trainer
A ten-module roadmap, in order
Open a module to see every topic and practical demo. The roadmap starts with a short prerequisites check, then runs Module 1 to Module 10 in order.
Module 1AI & ML Foundation
- AI Fundamentals & ML
What is AI? History of AI · Traditional Programming vs AI · Types & Branches of AI · What & Why Machine Learning? · Types of ML · AI vs ML vs DS - Modern .NET Foundation
Modern C# · ASP.NET Core Project · EF Core · Middleware · Dependency Injection (DI) · Repository Pattern · CRUD Operations - ML .NET
Deep Dive into ML · What is a Model? · Supervised Learning · Unsupervised Learning · Reinforcement Learning · Algorithms: Regression, Classification, Clustering · What & Why ML.NET? · Model Builder using ML.NET · Create Model, Train/Test Split, Predict · Consume Model from ASP.NET Core · Evaluation Metrics: R², MAE, MSE, RMSE · Overfitting, Underfitting · Confusion Matrix, Accuracy, Precision, Recall, F1 · Limitations of Classical ML
🛠 Demo: Random Forest Regression; Logistic Regression; Fast Tree; Light GBM
Module 2Deep Learning & Neural Networks
- Deep Learning Basics
What & Why Deep Learning? · Neural Networks · Perceptron · Layers · Activation Functions · Forward Propagation · Loss Function · Backpropagation · Optimization - CNN, RNN & LSTM
CNN: Convolution, Filters, Pooling, Image Concepts · RNN: Sequence Learning, Vanishing Gradients · LSTM · What & Why ONNX? · Load Existing Model · Fine-tuning & Retraining · Find Input/Output in Existing Model
🛠 Demo: Image Classification Project; Ticket Classification Project
Module 3NLP & Transformers
- NLP Fundamentals
What & Why NLP? · Text Preprocessing · Vocabulary · Tokenization · Stop Words · Stemming · Lemmatization · N-Grams · TF-IDF · Cosine Similarity - Attention Mechanism & Transformers
Limitations of Classical NLP · Attention · Self-Attention · Cross-Attention · Queries / Keys / Values · Encoder / Decoder · Transformer Architecture · Vectors, Embeddings, Tensors · Contextual Embeddings · BERT · GPT - Prompt Engineering & Conversation
System / User Prompts · Context · Few-Shot Prompting · Structured Output · History · Summarization · Evaluation of Prompts - Embeddings & Semantic Search
Mini LM (Generate Embeddings) · Dense Embeddings · Vector Representation · Cosine Similarity · Semantic Search · Ranking · .NET Implementation
🛠 Demo: Contextual Embedding using Mini LM
Module 4LLM Fundamentals & Integration
- LLM Fundamentals
What & Why LLMs? · Tokens · Context Window · Inference · Generation · Temperature · Capabilities & Limitations · GPT-Style Models - OpenAI / ChatGPT & Gemini Integration
OpenAI / ChatGPT APIs · Google Gemini · ASP.NET Core Integration · Structured Output · Streaming · Provider Abstraction · Options Pattern · Factory Design Pattern
🛠 Demo: Integrating Models using ASP.NET Core - Ollama & Local LLMs
Ollama · Local Model Setup · Model Selection · REST / API Integration · C# Integration · Local vs Cloud Models
🛠 Demo: Chat Application with Ollama
Module 5Vector Databases & RAG
- Vector Databases & RAG
Qdrant · Collections · Vectors · Metadata · Chunking · Top-K Retrieval · Hybrid Search · RAG · Enterprise Document Q&A
🛠 Demo: CRUD using Qdrant & C#; Storing & Retrieving Embeddings; Semantic Search & Hybrid Search (Document Search)
Module 6Advanced LLM Concepts
- Advanced LLM Architecture
ASP.NET Core + LLM Architecture · AI Services · Chat APIs · Memory / Context · SQL Persistence · Security · Observability
🛠 Demo: Conversation, Context, Conversation Summary - Tools, Function Calling & MCP
Function / Tool Calling · Schemas · External APIs · Database Tools · Controlled Business Actions · Orchestration · What is MCP? · MCP Client · MCP Server · MCP & RAG
🛠 Demo: Tools Calling, MCP Server, MCP Client
Module 7AI Agents & Agentic AI
- AI Agents & Agentic AI
What is an Agent? · What is Agentic AI? · Agent Fundamental Concepts · Instructions · Prompts · Tools · Memory
🛠 Demo: Agent using C# & ASP.NET Core - Microsoft Agent Framework (MAF)
What is MAF? · Various Frameworks · Semantic Kernel · Kernel / Services · Prompts · Plugins · Functions
🛠 Demo: Agent using MAF - MCP & Agentic AI
MCP Concepts · Tools / Resources / Prompts · MCP Workflows · Agent-Tool Architecture · Controlled Enterprise Integration
🛠 Demo: Add MCP to Agent
Module 8Enterprise AI Engineering
- Enterprise AI Engineering
Architecture · Security · Evaluation · Grounding · Hallucination Mitigation · Scalability · Logging · Monitoring · Deployment
Module 9Capstone & Career Readiness
- Capstone & Career Readiness
End-to-End AI Project · Architecture Review · Deployment · Documentation · Git / GitHub · Portfolio
Module 10Interview Preparation & Career Guidance
- Interview Preparation
How .NET to AI engineer interviews are structured · Explaining your projects and architecture decisions · Common mistakes and how to avoid them - Interview Questions
.NET and ASP.NET Core fundamentals · ML and ML.NET · Transformers, embeddings and LLMs · RAG and vector databases · Tool calling, MCP and agents · Enterprise AI: security, evaluation and hallucination mitigation - Resume Building
Positioning your .NET experience for AI roles · Showcasing course projects and the capstone · GitHub profile and portfolio - Career Guidance
Career paths: AI-enabled developer, AI application developer, AI engineer · Choosing roles and next learning steps
What you will actually build
4 ML.NET modelsRandom Forest, Logistic Regression, FastTree and LightGBM
Image & ticket classifiersDeep learning projects with ONNX
Semantic searchContextual embeddings with MiniLM in .NET
LLM chat appsOpenAI and Gemini in ASP.NET Core, plus local Ollama
Document search with RAGQdrant CRUD, embeddings, semantic and hybrid search
Tools and MCPTool calling, an MCP server and an MCP client
AI agentsIn C# and with Microsoft Agent Framework, plus MCP
CapstoneAn end-to-end, deployed, portfolio-ready AI project
Where you end up
Your experience plus AI engineering skills moves you up, not sideways.
.NET developerBuilds business applications
AI-enabled developerAdds LLM features to existing apps
AI application developerBuilds RAG and intelligent apps
AI engineerDesigns and ships enterprise AI systems
Built for working developers
Hands-on projectsEvery concept ends with working C# code.
Expert trainerAsk “why this approach?” and get a real answer.
Online and offline batchesWeekday and weekend options.
Career guidancePortfolio, GitHub and interview preparation.
Free for students
Join our free WhatsApp group
Missed the popup? Join now and get the roadmap, demo links and learning resources straight on your phone, even before you decide to enrol.
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- 📚Study materialNotes and resources shared in the group
- ▶️Video linksUseful learning videos for .NET developers moving to AI
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