Full Stack AI Engineer 2026 – Generative AI & LLMs III

Introduction

This course teaches how to design and build production-ready generative AI systems using large language models (LLMs). It is aimed at software and full-stack engineers, aspiring AI engineers, data practitioners, backend developers and technical founders who want hands-on, end-to-end skills. You will learn practical techniques—RAG, transformers, embeddings, agentic AI, tool calling, full-stack integration, cost and latency optimisation, and governance—through step-by-step labs and real code examples.

What you’ll learn & Who this is for

• What you’ll learn
  • Design and deploy production-grade LLM systems and architectures.
  • Implement Retrieval-Augmented Generation (RAG) pipelines with embeddings and semantic search.
  • Build agentic systems with tool calling, multi-step reasoning, and memory management.
  • Create full-stack LLM apps using FastAPI backends and streaming chat interfaces.
  • Optimise cost, latency and scalability via token optimisation, caching, and model selection.
  • Evaluate model outputs using human and automated checks for accuracy and faithfulness.
  • Apply security, safety and governance using guardrails, filters and policy controls.
• Who this is for
  • Software engineers and full-stack developers integrating LLMs into applications.
  • Aspiring AI engineers wanting job-ready, applied LLM skills.
  • Data engineers, data scientists, and ML engineers moving to end-to-end system design.
  • Backend/API developers building LLM-powered services and workflows.
  • Product engineers and technical founders designing scalable AI products.
• Prerequisites
  • Basic programming knowledge (Python preferred, but not mandatory at expert level).
  • General understanding of APIs or web applications (helpful, not required).
  • Curiosity about AI and willingness to build hands-on projects.

Course Overview & Syllabus Highlights

This is a practical, hands-on programme for engineers who want to move beyond toy prompts and build reliable, maintainable generative AI systems. The course focuses on modern components used in production: transformer fundamentals, LLM behaviour, embeddings and semantic search, RAG pipelines to ground model responses, agentic architectures with tools and memory, and full-stack deployment patterns using APIs and streaming interfaces. Each topic includes step-by-step labs so you implement working code, test real pipelines, and learn trade-offs for cost, latency and governance. Emphasis is on enterprise readiness: reducing hallucinations, adding human-in-the-loop controls, monitoring outputs, and applying security and policy guardrails. By the end you will be able to design, build and operate LLM-powered applications that are scalable, optimised and governed for real use.

• Intro: Generative AI & practical use cases
• Transformer architecture and LLM fundamentals
• Prompt engineering and function/tool calling
• Embeddings, semantic search and RAG pipelines
• Agentic AI, memory and human-in-loop controls
• Full-stack deployment: FastAPI, streaming UX, stateful memory
• Evaluation, optimisation, security and governance

How to Enrol, Study Tips, Alternatives & FAQ

How to Enrol / Claim Free Access

1. Visit the course page on the provider site (Data Science Academy, School of AI).
2. Check the listed price and curriculum details.
3. Apply any coupon or limited-time offer if available at checkout.
4. Check the price at checkout, free status can change.

Free status can change anytime. Please verify the price on the enrollment/checkout page.

Tips to Complete Faster

1. Block 5–8 hours per week; focus one module at a time with its lab.
2. Start with transformer fundamentals, then do a single RAG lab end-to-end.
3. Build a small project (chat + RAG) to apply prompts, embeddings and rollout steps.
4. Use notebooks and incremental commits so you can debug and revert quickly.

FAQ

• Is it really free? Not specified. Free access is not guaranteed and may change.
• Will I get a certificate? Not specified.
• How long will it stay free? Free status can change; please verify at checkout.
• Do I need expert ML skills? No. Basic programming and curiosity are the main prerequisites.
• Can this course help me build production apps? Yes—labs focus on production patterns like RAG, FastAPI, streaming and governance.

Conclusion

This practical course helps engineers build production-grade generative AI systems using LLMs, embeddings, RAG, agents, and full-stack deployment. You will gain hands-on experience with design choices, cost and latency trade-offs, evaluation techniques, and safety controls. Verify the course price before enrolling and confirm any free offers. Join our WhatsApp group for free course alerts