AI is an evolving and emerging skill, and it is very normal to lose track of the latest practices and trends to be job-ready in this particular domain. So, here we have prepared a selection of courses that can certainly help you keep up with the skillset demanded by companies in AI.
It is best to understand that AI engineer roles in 2026 commonly expect experience with machine learning models, large language models, retrieval systems, and production pipelines. Job descriptions now list skills such as model training and evaluation, RAG pipelines, prompt design, agent workflows, and cloud-based deployment as core requirements.
Because of this, the best AI engineer courses for you would be those that cover the full scope of modern AI engineering. This includes a strong foundation in machine learning and deep learning, along with applied topics such as generative AI, LLMs, agent-based systems, and production tooling. You can also check out the AI Engineer Roadmap to get a detailed insight into the curriculum and what you would generally cover.
The list we have prepared here covers AI engineer courses that focus on different parts of that skill set, including generative AI courses, LLM courses, RAG courses, AI agents courses, prompt engineering courses, and cloud programs like the Azure AI Engineer course and the Google Machine Learning Crash Course. Before the list, you can also review the Generative AI Roadmap and GenAI project ideas to understand the kind of portfolio work these courses help you build.
How We Chose These Courses (2026 Criteria)
The courses in this list were selected based on how well they prepare learners for current AI engineer responsibilities.
- Job relevance: Coverage of skills commonly expected in AI engineering roles, including LLM applications, RAG pipelines, agent workflows, evaluation practices, and deployment-oriented thinking.
- Hands-on proof: Presence of projects, labs, capstones, or assessments that require building and testing working systems.
- Clarity: A structured curriculum with clearly defined topics and learning outcomes, rather than loosely grouped lessons.
- Credibility: Courses offered by established providers, with certifications or recognized credentials where applicable.
- Time to portfolio: How quickly the course enables learners to ship two to three portfolio-ready projects that demonstrate applied AI engineering skills.
Top 10 Best AI Engineering Courses to Become Job-Ready
Before getting into the details of each course, here’s a quick overview of all the AI engineering courses covered in this guide.
| Course | Best for | Key coverage | Proof of work |
| Scaler x IIT Roorkee Advanced AI Engineering Course | Working professionals seeking a guided, end-to-end path | ML & DL foundations, generative AI, RAG systems, agent workflows, deployment thinking | Hands-on projects and a recognized IIT Roorkee CEC credential |
| IBM AI Engineering Professional Certificate | Learners building a broad ML + DL base | Machine learning with scikit-learn, deep learning with common frameworks | Labs and graded projects |
| DeepLearning.AI – Deep Learning Specialization | Strong deep learning fundamentals | Neural networks, optimization, CNN, and RNN basics | Assignments and course certificate |
| fast.ai – Practical Deep Learning for Coders | Developers learning through implementation | Practical deep learning workflows and applications | Notebooks and hands-on exercises |
| DeepLearning.AI / Coursera – Generative AI with LLMs | Introduction to LLM-powered applications | LLM fundamentals, application patterns, deployment concepts | Course certificate |
| DeepLearning.AI – Agentic AI | Building agent workflows and patterns | Agent loops, tool usage, multi-step task execution | Course certificate |
| Hugging Face – LLM Course | Hands-on work with the LLM ecosystem | Transformers, datasets, tokenizers, training workflows | Build-along chapters and exercises |
| Stanford CS224N | NLP and LLM depth (research-oriented) | Neural NLP, transformers, language model foundations | Assignments and a final project |
| Microsoft Certified: Azure AI Engineer Associate (AI-102) | Cloud-based AI deployment on Azure | Azure AI services, generative AI, production workflows | Official certification path |
| Google Machine Learning Crash Course | Free ML foundations or fast refresh | Core ML concepts with interactive practice | Built-in exercises and practice modules |
Now that you have skimmed through the list above, let’s get into the course details!
Part 1: Best End-to-End, Job-Ready Programs (Guided + Portfolio)
Courses in this section cover the full scope of AI engineering in a single program. They usually include machine learning and deep learning foundations with applied areas such as generative AI, RAG pipelines, and agent workflows, along with a clear focus on the portfolio.
These programs are a great choice for learners looking for an all-inclusive and detailed AI engineer course, especially if having a proper learning format and guided lessons helps you best.
1) Scaler x IIT Roorkee Advanced AI Engineering Course
This program is well-suited for professionals who want all aspects done in one platform, which includes machine learning foundations through generative AI, RAG systems, and agent-based workflows. The curriculum is organized to help learners move from model understanding to building complete AI systems, supported by a recognized credential.
Who it’s for
- Working professionals aiming to build and ship AI features or applications
- Engineers transitioning into AI product or platform roles
- Learners who want structure and outcomes rather than topic-by-topic learning
Key coverage
- Machine learning and deep learning foundations, focused on understanding and reasoning about models
- Large language models, prompt engineering, and RAG systems
- Agent workflows, tool usage, evaluation basics, and API integration
Proof of work
- Hands-on projects designed to be portfolio-ready
- Credential offered through an IIT Roorkee continuing education program
Also note:
- The program has an end-to-end structure, guided progression, and strong modern stack coverage
- It requires more time commitment than short or standalone courses
You can build a RAG-based knowledge assistant combined with a tool-calling agent, and include basic evaluation and monitoring notes. You can explore similar ideas in Scaler’s Generative AI Projects and connect this work to deployment concepts through the System Design course.
Check out Scaler x IIT Roorkee AI Engineering Course with GenAI and LLMs for further details!
2) IBM AI Engineering Professional Certificate
This program focuses on building a solid foundation in machine learning and deep learning through a structured set of courses. It works well for learners who want a broad technical base before moving into areas like generative AI, RAG, or agentic systems.
- The program covers machine learning using scikit-learn
- Introduces deep learning with frameworks such as Keras, PyTorch, and TensorFlow
- Includes labs and graded projects to reinforce core concepts
By the end of the course, you can add a generative AI layer by building a small RAG-based chatbot on top of one of the datasets used in the course.
Part 2: Best Foundations (ML + Deep Learning)
Courses in this section focus on machine learning and deep learning fundamentals, which remain essential for AI engineering roles. These programs are useful if you want a solid understanding of how models are trained and optimized before moving into generative AI, RAG pipelines, or agent-based systems.
3) DeepLearning.AI – Deep Learning Specialization
This specialization is designed for learners who want a strong conceptual base in deep learning before working with LLMs or advanced generative systems. It focuses on how neural networks are structured and trained.
- The course covers neural networks, optimization techniques, and core architectures.
- Introduces CNN and RNN fundamentals used across vision and sequence tasks
- Includes graded assignments to reinforce learning
You can pair this with an LLM or RAG-focused course, followed by an AI agents course to apply these foundations in system-level workflows.
4) fast.ai – Practical Deep Learning for Coders
This course takes a practical approach to deep learning by starting with working models and refining them through experimentation. It suits learners who already code and prefer learning through implementation.
- The course focuses on practical deep learning applications and workflows
- Uses notebooks and hands-on exercises throughout the course
- Emphasizes experimentation and iteration
You can try to ship a small deep learning project and deploy it as a simple API or demo before moving on to a generative AI or RAG course.
Part 3: Best for LLM Apps (GenAI + RAG + Agents)
Courses in this section focus on building applications using large language models. They cover how LLMs are used in practice, including prompt design, retrieval-based pipelines, and agent workflows that operate over tools and data. These courses are relevant once basic ML and deep learning foundations are in place.
5) DeepLearning.AI / Coursera – Generative AI with LLMs
This course introduces large language models and their use in application development. It covers how LLMs are accessed through APIs and how they are integrated into common generative AI workflows.
- LLM fundamentals and usage patterns
- Application-level concepts related to deploying LLM-powered features
- Coverage aligned with common generative AI use cases
You will be able to build a prompt library, add a simple RAG pipeline, and create a basic evaluation harness to test outputs.
6) DeepLearning.AI: Agentic AI
This course focuses on agent-based workflows where LLMs are used to complete multi-step tasks. It introduces common agent patterns used for tool usage and task execution.
- The program includes Agent loops and task decomposition
- Tool usage within agent workflows
- Multi-step execution patterns
Try to extend an agent with retrieval and tools, such as a research agent that produces a summary and then performs an action like creating a ticket or drafting an email.
7) Hugging Face: LLM Course
This course provides practical exposure to the modern LLM ecosystem. It covers the tools commonly used to train, adapt, and run language models in practice.
- You will be learning Transformers, datasets, and tokenizers
- Training and acceleration workflows
- Practical build-along chapters
You can try to fine-tune or adapt a model, evaluate its outputs, and package the result as a small demo or application.
Part 4: Best for NLP and LLM Depth (Research-Grade)
Courses in this section focus on natural language processing fundamentals and language model research. They are suited for learners who want a deeper understanding of how modern NLP systems and LLMs are designed, trained, and evaluated, beyond application-level usage.
8) Stanford CS224N: Natural Language Processing with Deep Learning
This course covers the foundations of modern NLP with an emphasis on neural approaches. It introduces core concepts that underpin transformer models and large language models, along with research-oriented perspectives on language understanding.
- Neural NLP foundations and representation learning
- Transformers and concepts central to modern LLMs
- Assignments and a course project
- Graded assignments and a final project based on NLP or language modeling tasks.
After completing this course, you can translate a research concept from the course into an applied project, such as a RAG pipeline with evaluation to test retrieval quality and output grounding.
Part 5: Best for Production (Cloud + Deployment)
Courses in this section focus on deploying and operating AI systems in production environments. They cover cloud services, model integration, security, monitoring, and cost considerations that are commonly part of AI engineer roles in the industry.
9) Microsoft Certified: Azure AI Engineer Associate (AI-102)
This certification program focuses on building and deploying AI solutions using Azure services. It covers how AI models and generative AI components are integrated into cloud-based applications with operational considerations.
- Azure AI services and platform components
- Generative AI and agent-related capabilities within Azure
- Production-oriented workflows, including deployment and monitoring
- A defined certification path supported by official training resources and assessments.
You can also try to deploy a RAG-based application with authentication, logging, and basic cost controls enabled.
10) Google – Machine Learning Crash Course
This course provides a practical introduction to machine learning concepts through short lessons and interactive exercises. It is structured to help learners understand core ML ideas and apply them quickly.
- The course provides core machine learning concepts explained through examples.
- Interactive exercises and practice modules
- Lightweight and self-paced format
- Built-in interactive practice exercises to help learn concepts better.
By the end of the course, you will be able to move into deep learning and LLM-focused courses, then progress toward application and agent-building tracks covered in earlier parts.
How to Choose the Right Course
If you’re unsure where to begin, the easiest way to decide is to match your current goal with the type of course that fits it best.
- If you want one structured program that covers ML, generative AI, RAG, and agent workflows in one path, the Scaler x IIT Roorkee Advanced AI Engineering Course is designed for that end-to-end progression.
- If your priority is building strong machine learning and deep learning foundations, courses like IBM AI Engineering Professional Certificate, DeepLearning.AI’s Deep Learning Specialization, or fast.ai’s Practical Deep Learning for Coders provide the required base.
- If you want to build LLM-powered applications, including prompt-driven features, RAG pipelines, and agent workflows, courses such as Generative AI with LLMs by DeepLearning.AI, DeepLearning.AI’s Agentic AI, and the Hugging Face LLM Course fit this stage.
- If you are looking for deeper NLP and LLM understanding from a research perspective, Stanford CS224N offers a strong theoretical and architectural grounding.
- If your role involves deploying AI systems in production or on cloud platforms, the Microsoft Certified Azure AI Engineer Associate (AI-102) focuses on cloud-based AI services and deployment practices.
- If you want a free or low-commitment starting point, the Google Machine Learning Crash Course works well for ML basics, while free agent-focused courses can be added later as you progress.
You can choose a course based on your current role and skill gaps, and add other courses later as your responsibilities expand.
Portfolio Projects That Make You Job-Ready Faster
The projects below focus on skills that commonly appear in AI engineer roles, such as working with LLMs, retrieval systems, agents, and deployment. Each idea is scoped so it can be completed and explained clearly in a portfolio.
- RAG Assistant over Documents: Build a retrieval-based assistant that works over company documents or course notes. Include chunking, embeddings, retrieval logic, and citations so outputs can be traced back to the source text.
- Tool-Using Agent Workflow: Create an agent that follows a fixed flow, such as search – summarize – action. For example, the agent searches for information, summarizes the result, and then triggers an action like sending an email or creating a task.
- LLM Evaluation Harness: Implement basic evaluation checks for LLM outputs. This can include hallucination detection, retrieval correctness checks, and simple pass/fail tests for expected behavior.
- Fine-Tuning or Adapter Experiment: Run a small experiment using fine-tuning or parameter-efficient adapters on a limited dataset. Document dataset choice, training setup, and before-and-after behavior.
- Deployed Inference Endpoint: Deploy a model or LLM-powered feature behind an API. Add basic monitoring such as request logging, latency tracking, and simple error handling.
- Prompt Library with Tests: Build a prompt library for a specific task and pair it with a small test suite. Track prompt versions and document how changes affect outputs.
- Mini AI Support Bot: Create a support bot that answers questions and creates tickets when needed. Include tool calls, simple routing logic, and clear criteria for when a ticket is generated.
These projects are small enough to complete individually, but together they demonstrate applied skills across LLM usage, retrieval, agents, evaluation, and deployment.
FAQs
Which AI engineer course is best for working professionals?
For working professionals, the most suitable AI engineer course is an all-inclusive program that includes teaching the latest skillsets and help developing a great portfolio. Programs like Scaler’s AI Engineering with IIT Roorkee provide such learning formats. Curriculum-wise, check if they have machine learning foundations with generative AI, RAG pipelines, and agent workflowsthat work best, especially when they are designed to fit alongside a job and focus on applied projects rather than only assessments.
Should I learn machine learning before LLMs?
Yes, basic machine learning knowledge is strongly recommended before moving into LLMs. Understanding concepts such as data preparation, training, evaluation, and overfitting makes it easier to reason about LLM behavior, RAG systems, and model limitations. Many learners study ML and deep learning alongside an LLM course rather than treating them as completely separate steps.
What is RAG, and why do jobs need it?
RAG, or retrieval-augmented generation, is a technique where an LLM retrieves relevant information from documents or databases before generating an answer. Jobs use RAG because it allows AI systems to work on internal data, produce grounded responses, and reduce incorrect or unsupported outputs. It is commonly used in support tools, internal assistants, and knowledge-based applications.
How do I build AI agents safely?
Building AI agents safely involves limiting what tools an agent can access, validating inputs and outputs, and adding checks around sensitive actions. Common practices include permission-based tool use, clear refusal rules, logging agent decisions, and testing agent behavior using predefined scenarios before deployment.
How long until I’m job-ready?
The timeline depends on your starting point and how much time you can dedicate. Learners with a software background often reach job-ready skills in six to nine months by combining a solid AI engineering course with hands-on projects. Those starting from scratch may take longer, especially if they need to build ML and programming foundations first.
What projects impress recruiters for AI engineer roles?
Recruiters look for projects that show applied problem-solving. Projects like RAG-based assistants over documents, tool-using agents that complete multi-step tasks, evaluation setups that test model behavior, and deployed APIs with basic monitoring. Clear documentation explaining design choices and limitations is often as important as the code itself.