Generative AI Engineering with OpenAI API and AWS Models

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Generative AI is the most in-demand skill in technology, but using it is different from building with it. This Specialization gives you the skills to design, build, and deploy production-grade AI systems — from your first OpenAI API call to enterprise-scale AWS architectures governed by responsible AI principles. Across three progressively advanced courses, you'll gain hands-on proficiency with modern AI engineering tools and frameworks. You'll configure the OpenAI platform and apply prompt engineering techniques, including zero-shot, few-shot, and grounding strategies. You'll build vision AI applications using DALL-E and CLIP, and implement advanced API capabilities — function calling, structured outputs, and batch processing — that separate prototype applications from scalable, production systems. You'll then architect Retrieval-Augmented Generation (RAG) pipelines and multi-step AI agents on AWS using Amazon Bedrock and vector databases. The Specialization closes with a responsible AI governance module covering bias mitigation, explainability, legal risk, and human-centered design using AWS tools — a competency now demanded by enterprises and regulators alongside technical proficiency. By the end, you'll have a portfolio of deployable AI applications: a text-based AI assistant, an image generation and captioning pipeline, and a full RAG system with a documented AI governance assessment.

Syllabus

Course 1: Generative AI: OpenAI API and Prompt Engineering
Course 2: Vision AI and Advanced OpenAI
Course 3: AWS GenAI: RAG, AI Agents and Responsible AI

Courses

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Deploying generative AI applications reliably, ethically, and at enterprise scale is a significant challenge. This course provides the advanced AWS architecture skills and responsible AI governance frameworks needed to implement trusted systems. You will master the production toolkit for foundation models on AWS, including model selection, Amazon Bedrock configurations, Retrieval-Augmented Generation (RAG) with vector databases, multi-step AI agents, prompt engineering, fine-tuning decision frameworks, and LLM benchmarking. A comprehensive responsible AI module addresses bias mitigation, dataset curation, explainability, legal risk management, and human-centered design through workplace role-play scenarios using AWS tools. The course concludes with a Python capstone project: building a production-ready Multimodal AI Knowledge Assistant that integrates text/image generation, function calling, a RAG pipeline, structured outputs, content moderation, and a documented governance layer. This course is designed for ML engineers, cloud architects, data scientists, and AI governance professionals with OpenAI API experience ready to deploy and govern enterprise AI systems on AWS.
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Generative AI is reshaping every industry — and building with it starts here. This course gives you the practical foundation to engineer AI-powered applications immediately, combining a clear understanding of how large language models work with hands-on OpenAI API development and real project work. You'll begin by setting up the OpenAI platform: account configuration, secure API key generation, model selection, and library navigation. You'll then explore the mechanics of modern generative AI — how transformers and attention mechanisms power LLMs, how tokenization shapes model behavior, and how prompt engineering techniques including zero-shot, few-shot, and grounding strategies improve output accuracy. You'll also examine bias, fairness, and reinforcement learning, giving you the perspective needed to build responsibly. In the final module, you'll build three working applications: a recipe generator, an article translator, and an AI research assistant using the Chat Completions API — applying fine-tuning, embeddings, and text-to-speech through live project work. Designed for software engineers, data scientists, IT professionals, and career changers entering the AI field. Basic programming knowledge is recommended. No prior AI experience is required.
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Once you can build with text, the next step is multimodal AI and enterprise-scale capabilities. This course advances your OpenAI skills into image generation, advanced production API features, and the foundational AWS architecture knowledge you need to design serious generative AI systems. You'll start with OpenAI's vision capabilities: how DALL-E generates images from natural language prompts, the evolution from DALL-E 1 to DALL-E 3, and how CLIP connects visual and language understanding. You'll build a working image generator and an image captioning pipeline, and examine the ethical challenges and future trends shaping AI vision technologies. Next, you'll implement OpenAI's most powerful production features — function calling, structured outputs, batch processing, and content moderation — the capabilities that separate prototype applications from scalable, enterprise-grade systems. The course closes with a critical knowledge bridge into AWS: tokens, embeddings, chunking, context windows, the foundation model lifecycle, AWS GenAI infrastructure design, and cost optimisation principles — preparing you for AWS-native deployment. Designed for learners who have OpenAI API experience. Basic programming knowledge is recommended.