AI Engineering Certification Course & Training Program

Build the core groundwork for a career in AI engineering, covering machine learning fundamentals, neural network basics, and the AI development lifecycle. This module sets up the conceptual foundation you'll need before working with large language models, generative AI systems, and production-grade AI applications.

Understand the transformer architecture that powers modern large language models (LLMs), including attention mechanisms, tokenization, and how models like GPT and Llama are trained. Ideal for learners who want a solid technical grasp of LLM internals before building real-world generative AI applications.

Learn to design, build, and deploy applications powered by large language models. This module covers LLM APIs, application architecture patterns, integration best practices, and how to turn foundation models into production-ready AI-powered software.

Master prompt engineering techniques including few-shot prompting, chain-of-thought, and structured prompting alongside model adaptation methods like fine-tuning and parameter-efficient tuning. Learn how to optimize LLM outputs for accuracy, consistency, and task-specific performance.

Explore how embeddings represent text, images, and other data as vectors, and how vector databases (like Pinecone, Weaviate, and FAISS) enable fast semantic search. A foundational module for anyone building retrieval-based or context-aware AI systems

Learn to design and build Retrieval-Augmented Generation (RAG) pipelines that ground LLM responses in real, up-to-date data. This module covers chunking strategies, retrieval methods, and integrating vector search with generative models to reduce hallucinations and improve accuracy.

Go beyond basic RAG with advanced retrieval architectures hybrid search, re-ranking, multi-hop retrieval, and query transformation techniques. Designed for engineers who want to build enterprise-grade RAG systems that scale and perform reliably in production.

Learn how to design autonomous AI agents that can reason, plan, and use external tools and APIs to complete complex tasks. This module covers agent architectures, function calling, and orchestration patterns essential for building intelligent, action-oriented AI systems.

Combine agentic AI with retrieval-augmented generation and the Model Context Protocol (MCP) to build systems that can dynamically retrieve, reason, and act. Learn how MCP standardizes tool and data access for more capable, interoperable AI agents.

Learn how to evaluate LLM outputs for quality and reliability, and implement guardrails to prevent prompt injection, data leakage, and unsafe behavior. This module covers AI security best practices essential for deploying trustworthy, enterprise-ready AI systems.

Understand how to operationalize large language models at scale covering deployment, monitoring, cost optimization, versioning, and CI/CD for AI systems. This module bridges the gap between building LLM applications and running them reliably in production.Apply everything you've learned across the program in an end-to-end capstone project designing, building, and deploying a real-world generative AI application.