AI Engineer

Focus your path to building enterprise-ready AI solutions

Your organization looks to you for practical, production-ready AI solutions. 

As companies adopt generative AI to automate processes and drive business impact, IT professionals and software engineers are increasingly expected to deliver scalable, reliable, and secure AI applications. This AI Engineer specialization equips you with the skills to design, build, deploy, and maintain real-world solutions powered by large language models (LLMs), retrieval-augmented generation (RAG), and AI agents.

You’ll learn to integrate AI into workflows, automate tasks, analyze and summarize data, and build applications that deliver measurable value—from customer support automation and feedback analysis to research acceleration and workflow optimization. Emphasis is placed on industry-relevant practices such as prompt engineering, vector database integration for RAG, context engineering, LLM application monitoring and evaluation, and AI safety guardrails for responsible deployment.

Through hands-on, project-based courses, you'll create deployable, maintainable AI solutions ready for use across the organization. Lead the change in any department—from marketing and HR, to customer service, and R&D.

Learning outcomes

After completing the AI Engineer specialization students should be able to:

• Explain the architecture and functionality of LLMs, including transformer architecture, embeddings, pre-training and next token generation.
• Develop, deploy, and integrate LLM-powered applications using APIs and frameworks like LangChain for tasks such as customer support, productivity, and feedback analysis.
• Design and implement RAG-based applications to retrieve, summarize, and contextualize internal knowledge while evaluating performance with standard metrics.
• Build AI agents capable of reasoning, planning, automating workflows, and integrating with tools, APIs, and data systems for business applications.
• Evaluate, monitor, and improve generative AI systems using best practices, ensuring ethical, secure, and responsible deployment.