The world of AI engineering is moving incredibly fast. Every week brings new models, techniques, and breakthroughs. But beneath all that chaos, there are sophisticated patterns and architectural principles that remain consistent across implementations.

I recently published a new LinkedIn Learning course: Fundamentals of AI Engineering: Principles and Practical Applications.

Why This Course Matters

After building mission-critical systems at companies like Palantir and Citadel, I’ve learned that the gap between AI research and production-ready systems is often wider than expected. This course bridges that gap by focusing on the engineering fundamentals that actually matter in production environments.

This isn’t another theoretical AI course. It’s designed for software engineers who want to build AI systems that scale, perform reliably, and solve real business problems.

Course Approach

Hands-On Implementation: Everything is built using open-source tools like LlamaIndex and Hugging Face. The course uses real code, real data, and real challenges rather than theoretical examples.

Production-First Mindset: The focus is on systems that can handle real-world loads, not just demo scenarios.

GitHub Codespaces Integration: Students can start coding immediately without environment setup complexity.

Course Deep Dive

Foundation: Local LLM Operations

We start by running large language models locally, understanding the complete pipeline from tokenization to inference. You’ll learn to move beyond the API-driven approach and understand what’s actually happening under the hood.

Document Processing at Scale

Real-world AI applications need to handle messy, unstructured data. We cover:

  • Advanced text extraction techniques
  • Structure recognition and metadata enrichment
  • Optimal chunking strategies for different document types
  • Performance considerations for large document corpuses

The Embedding Ecosystem

Embeddings are the foundation of modern AI retrieval systems. You’ll master:

  • Comparing and selecting embedding models for your use case
  • Efficient embedding generation and batch processing
  • Understanding the trade-offs between speed, accuracy, and cost

Vector Database Mastery

Moving beyond simple similarity search to production-grade vector operations:

  • Database selection and optimization
  • Approximate Nearest Neighbor (ANN) algorithms
  • Caching strategies for performance
  • Scaling considerations and cost management

Advanced Retrieval Engineering

This is where the magic happens. We build sophisticated retrieval systems that combine:

  • BM25 and vector search for comprehensive coverage
  • Hybrid retrieval that leverages the strengths of both approaches
  • Cross-encoder reranking for precision improvements
  • Complete pipeline integration with monitoring and observability

What You’ll Learn

This 4+ hour course covers:

  • Building production-ready RAG systems using embeddings and vector database pipelines
  • Implementing monitoring and observability for AI applications using telemetry tools
  • Creating efficient document processing pipelines with hybrid search capabilities
  • Designing CI/CD workflows for deploying and testing AI applications
  • Optimizing AI system performance and costs through caching and resource management

Who Should Take This Course

This course is perfect for:

  • Software engineers looking to add AI capabilities to their toolkit
  • Backend developers who want to understand AI system architecture
  • Technical leaders planning AI implementations
  • Anyone building production AI applications who needs to go beyond simple API calls

The course assumes intermediate programming knowledge but doesn’t require prior AI experience.

Real-World Applications

Throughout the course, we build systems that mirror real production challenges:

  • Enterprise document search and retrieval
  • Customer support automation
  • Knowledge base augmentation
  • Multi-modal content processing

Key Takeaways

AI engineering isn’t just about calling APIs or fine-tuning models. It’s about building reliable, scalable systems that solve real problems. The course focuses on developing the engineering judgment needed to build AI systems that actually work in production.

The field is moving fast, but the fundamentals remain constant. Understanding these patterns provides a solid foundation for whatever comes next in AI development.

You can find the course on LinkedIn Learning.

Questions about the course content or AI engineering in general? Feel free to reach out – I love talking about this stuff.