AI Engineer is LinkedIn's fastest-growing job, and AI can't scale without people who can run it. If you work with servers, networking, storage, VMs or containers, you already have the hardest part. Choose one of three paths (AWS AI/ML Engineer, Azure AI ML Engineer or Agentic AI Engineer) and become the person who makes AI work in production. Cloud background helpful but not required, and all the Python you need is taught inside the program.
46,000+ professionals trained by K21 Academy · Live interactive sessions · 1-year access · Unlimited retakes
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AI teams have plenty of people who can prompt a model. What they lack is people who can deploy it, secure it, scale it and keep it running. Here is what your infrastructure experience already gives you, and the short list of what you add.
* No cloud experience? No problem: cloud deployment is covered inside the program.
Our coaches and alumni work at these companies
Each path keeps the infrastructure you know at its core and adds the AI layer on top. Pick a path to see the full job-oriented program, the certifications it prepares you for, and the beginner and Python bonuses that come with it.
Full Solutions Architect scope + AI/ML layer, using the AWS services you already know.
17 modules · the core of the path, from GenAI on AWS to MLOps and governance
AI, ML, and Gen AI, Artificial Intelligence, Machine Learning and more
9 topicsGenerative AI, GenAI services on AWS, GenAI use cases and more
11 topics · 1 labIntroduction to Amazon Bedrock, Accessing Bedrock using the console, Transformers and more
6 topics · 4 labsAnatomy of a prompt, Core prompting techniques, Prompt engineering master cheat sheet
3 topics · 3 labsIntroduction to RAG, RAG concepts, architecture & embeddings intro, Vector embeddings & search and more
5 topics · 6 labsNon-agentic vs agentic workflows, Compound AI systems & a manufacturing use case, AI application development lifecycle and more
4 topics · 5 labsData integrity & transformation, Feature transformation techniques, Feature engineering methods and more
4 topics · 1 labDeployment strategies, Deploying models with SageMaker, Monitoring and maintenance
3 topics · 2 labsContent moderation & safety policies, Securing data with encryption, PII management & data compliance and more
5 topicsAmazon Comprehend, Amazon Lex, Amazon Translate and more
12 topics · 11 labsObject storage options, Simple Storage Service (S3), S3 bucket policy and more
6 topics · 5 labsData transformation, AWS Glue, SageMaker Feature Store and more
4 topics · 4 labsIntroduction to data analysis, Time series: trends and seasonality, Overview of Amazon QuickSight and more
7 topics · 1 labModel training, Deep dive into model training, Distributed training and more
5 topics · 3 labsSageMaker Studio, MLflow & Kiro, SageMaker architecture & the ML lifecycle, SageMaker and built-in algorithms
3 topics · 2 labsAWS containers & Docker, Amazon EKS, CloudFormation
3 topics · 3 labsResponsible AI, Augmented AI, Security, compliance, and governance in GenAI and more
12 topicsSolutions Architect first, then Generative AI Developer, then Machine Learning Engineer
AWS Certified Solutions Architect, Associate (SAA-C03)
10 modules · 21 labsDesign, govern and scale enterprise solutions: security, storage, compute, networking, databases and the Well-Architected Framework.
AWS Certified Generative AI Developer, Professional (AIP-C01)
11 modules · 13 labsBuild, integrate and ship production GenAI apps: Amazon Bedrock, RAG, agents, fine-tuning, guardrails and deployment.
AWS Certified Machine Learning Engineer, Associate (MLA-C01)
11 modules · 14 labsIngest data, engineer features, train, tune, deploy and operate ML models on SageMaker, plus MLOps, security and governance.
Two foundational AWS exams to get you familiar with the cloud and AI on AWS
Cloud concepts, core services, security, pricing and billing: full exam prep.
5 topics · 2 labsAI/ML on AWS, Amazon Bedrock, generative AI and responsible AI: full exam prep.
5 topics · 2 labsBeginner sessions, included for everyone. Take them before the program or alongside it.
Cloud concepts across AWS, Azure, Google Cloud and Oracle.
Core AWS services, pricing, free tier and billing set-up.
AI, ML, deep learning, GenAI and LLMs explained.
Hands-on with SageMaker, Bedrock and AI APIs, plus troubleshooting.
From your first script to the libraries behind AI and ML.
Overview of Python, environment set-up and the basics.
Tuples, lists, dictionaries, sets, lambdas, exceptions and file I/O.
Classes, inheritance, encapsulation, polymorphism and decorators.
ML workflow, NumPy, Pandas, data cleaning and EDA.
Encoding, scaling, PCA and preprocessing pipelines.
Regression, classification and evaluation metrics.
Decision trees, bagging, boosting and tuning.
Clustering, dimensionality reduction and anomaly detection.
Coding needed: LOW, mostly config, console and API calls. Your SAA-C03 infrastructure knowledge is directly reused across every AI service.
Full Azure Solutions Architect scope + AI/ML layer: huge demand in enterprise Microsoft environments.
12 modules and 70 hands-on labs, from Foundry and agents to MLOps, GenAIOps and governance
AI & ML Fundamentals, Generative AI & LLMs, AI Agents & Agentic Workflows and more
6 topics · 8 labsGenAI & LLMs in Foundry, Prompting & Grounding, Foundry IQ & Knowledge Apps and more
6 topics · 6 labsChat App Architecture, Prompt Flow & Context, Tool & API Integration and more
6 topics · 4 labsAgentic & Multimodal Systems, Responsible AI & Safety, Reliable & Secure Agents and more
6 topics · 6 labsAgentic Workflows in Foundry, Multi-Agent Solutions, A2A Protocol & Remote Agents
3 topics · 3 labsSpeech-Capable GenAI, Designing Voice Agents, Text & Speech Translation
3 topics · 4 labsComputer Vision Basics, Document Intelligence, Content Understanding
3 topics · 4 labsCopilot Studio Design, Topics, Knowledge & Flows, Publishing Copilots
3 topics · 3 labsGit & GitHub for AI, Branching & Code Review, GitHub Actions CI/CD and more
6 topics · 6 labsAzure ML Pipelines, Preparing an MLOps Project, Automated Training (GitHub Actions) and more
6 topics · 7 labsGenAIOps & App Lifecycle, Prompt Versioning, Automated Evaluation and more
6 topics · 7 labsGovernance & Compliance, Responsible AI & Risk, Security & Privacy
3 topicsAzure Fundamentals and Azure AI Fundamentals to get you familiar with the platform
Cloud concepts, core Azure services, pricing, SLAs, governance and compliance, full exam prep.
29 topics · 2 labsAI workloads, ML principles, vision, NLP, GenAI, Azure OpenAI and responsible AI, full exam prep.
24 topics · 8 labsCertifications you are eligible to sit for
Included for every learner
Deploy and scale AI/ML workloads on Azure Kubernetes Service.
2 topicsAutomated ML pipelines with CI/CD for training and deployment.
3 topicsBeginner sessions, included for everyone. Take them before the program or alongside it.
What cloud computing is and why it matters, and the tools you set up first.
Azure portal, subscriptions, resource groups, governance and support.
AI, ML, deep learning, GenAI and Microsoft Foundry, plus the API keys you need.
The ML lifecycle, MLOps tooling and infrastructure as code on Azure.
From your first script to the libraries behind AI and ML.
Python set-up, syntax and the basics of machine learning.
Data structures, control flow, functions, testing and API calls.
Objects, inheritance, polymorphism and encapsulation.
Pre-processing, Pandas, NumPy, Matplotlib and scikit-learn.
Feature engineering and dimensionality reduction.
Regression, classification and choosing the right metric.
Decision trees, Random Forest, XGBoost and classification reports.
Clustering, association rules and pattern mining.
Coding needed: LOW, Azure AI Foundry and ML Studio are largely GUI-driven. Your Azure infra knowledge makes this the most natural transition.
Highest demand right now, and the most future-proof path.
10 modules, from LLM foundations to multi-agent systems in production
Transformer architecture, Embeddings and vector spaces, Multi-provider LLM setup and more
5 topics · 1 labZero-shot and few-shot prompting, Chain-of-Thought, ReAct and more
6 topics · 1 labLangChain LCEL pipeline, Loaders and chunking, Embeddings and more
8 topics · 1 labAgent vs. chatbot vs. chain, The agent loop, Tool use and the @tool decorator and more
5 topics · 1 labStateGraph, TypedDict reducers, Conditional edges and routing and more
7 topics · 1 labSupervisor, Swarm, Orchestrator-Worker and more
6 topics · 1 labOpenAI Agents SDK (handoffs, guardrails), Real-time voice agent with semantic VAD, CrewAI crews and more
5 topics · 1 labContext budgeting, JIT retrieval, Compaction and more
6 topics · 1 labFour-layer guardrail stack, Presidio PII detection, LangSmith and Langfuse tracing and more
5 topics · 1 labNotebook-to-production workflow, FastAPI streaming endpoints, Chainlit and Streamlit UIs and more
5 topics · 1 labEight pillars for securing agents in production
Eight pillars for securing agents in production.
8 topicsAWS and Azure AI fundamentals to back your agentic skills
AI/ML on AWS, Amazon Bedrock and responsible AI: full exam prep.
6 topics · 2 labsAI workloads, Azure OpenAI and Microsoft Foundry: full exam prep.
6 topics · 2 labsBeginner sessions, included for everyone. Take them before the program or alongside it.
Cloud concepts across AWS, Azure, Google Cloud and Oracle.
Core AWS services, pricing and billing controls.
Compute Engine, Cloud Storage, Cloud SQL, IAM, VPC and billing.
OCI compute, storage, Autonomous Database, IAM and networking.
AI fundamentals, LLMs and what makes AI agentic.
Data types, storage, ETL and cloud data services.
From your first script to the libraries behind AI and ML, plus the PCEP™ certification.
Overview of Python, environment set-up and the basics.
Tuples, lists, dictionaries, sets, lambdas, exceptions and file I/O.
Classes, inheritance, encapsulation, polymorphism and decorators.
ML workflow, NumPy, Pandas, data cleaning and EDA.
Encoding, scaling, PCA and preprocessing pipelines.
Regression, classification and evaluation metrics.
Decision trees, bagging, boosting and tuning.
Clustering, dimensionality reduction and anomaly detection.
The entry-level Python certification: full exam prep included.
All required Python and coding is taught as part of the program, no prior coding needed. Your infra skills make you learn faster than most.
| AWS AI/ML Engineer | Azure AI ML Engineer | Agentic AI Engineer | |
|---|---|---|---|
| Coding needed | Low | Low | Medium |
| Uses infra skills | Very High | Very High | High |
| Demand right now | High | High | Very High |
| Projects included | AWS AI industry projects | Azure AI industry projects | Multi-cloud agentic projects |
| Best for | AWS background | Microsoft / enterprise | Cutting edge / future |
| Time to job-ready* | 12-16 weeks | 12-16 weeks | 12-18 weeks |
| Certs included | CLF-C02, SAA-C03, AIF-C01, MLA-C01, AIP-C01 | AZ-900, AI-901, AZ-305, AI-103, AI-300 | PCEP™ (Python Institute), AIF-C01, AI-901 |
* Timing varies by effort level: some complete faster. Average based on part-time study.
Hand-picked from the AWS AI, Azure AI and Agentic AI programs: the projects about deploying, scaling, securing and operating AI the work infrastructure engineers are hired for. Tap a card to flip it and see the architecture.
“Do I need coding and programming? I do not have that background.” It's the most common question on all three tracks, and the answer is no. All Python and coding is taught inside the program, and cloud deployment is covered if you have only worked on-premise.
Most people pick this up in 4 to 6 weeks. Your infra knowledge is the hardest part to teach: that is the gap most AI engineers have.
Every track comes with its certifications covered in the curriculum: 3 to 5 per track. Take two tracks or all three and you collect the certifications from each.
The AWS route: start at Cloud Practitioner, reach Solutions Architect, then add the AI, ML and Generative AI Developer credentials on top.
The Microsoft route: Azure and AI fundamentals, the Architect Expert credential, then the AI App and Agent Developer and MLOps Engineer certifications.
The multi-cloud route: the Python Institute PCEP™ credential for the coding you learn, plus the AWS and Azure AI fundamentals.
Key insight: companies are desperate for people who can BUILD and RUN AI systems, not just model them. That is exactly what an infra background prepares you to do.
About 25 years in IT as a multi-cloud, data and AI architect. His own career includes Oracle DBA, Apps DBA and data engineer roles, so he knows the move from infrastructure into AI from the inside. K21 Academy has trained 46,000+ professionals.
Clients across his career include:
★ Trustpilot
★★★★★As a VP of Software Engineering making a deliberate career transition into AI/ML and Cloud leadership, I've been extremely selective about where I invest my time and money. Enrolling in K21 Academy's program has been one of the best professional decisions I've made this year. I've completed four courses and passed three Microsoft certification exams: AI-900, AZ-900, and AB-731.
★ Trustpilot
★★★★★Very pleased with K21 Academy thus far. The instructor of the Agentic AI Mastery program is very knowledgeable and a very good communicator. The team in the Interview/Job Preparation area are extremely helpful in assisting with resume modification, LinkedIn profile optimization, and job application preparation. I highly recommend K21 Academy.
★ Trustpilot
★★★★★I have been part of a few training programs by K21 Academy and they are doing justice with what they are offering. They have a detailed program structure, professional trainers and their support system is excellent. The learning portal SKOOL is the real differentiator. I would recommend K21 Academy to all serious learners.
★ Trustpilot
★★★★★I wanted to express my special appreciation to Atisha and the team for their constant support throughout my job search and interview preparation journey. The interview guides have been invaluable study material and remarkably accurate in reflecting the types of questions asked during my interviews. It has made a real difference to my preparation and confidence.
★ Trustpilot
★★★★★I had a great experience learning AI/ML and Cloud Engineering concepts with K21 Academy. The training was very practical and helped me strengthen my skills in Python, cloud technologies, data pipelines, and real-world projects. Career support included resume building, LinkedIn optimization, interview preparation. Highly recommend.
★ Trustpilot
★★★★★Thank you for arranging the mock interview and for helping me better understand what the company is looking for. The discussion gave me much greater clarity about the role and its expectations. The mock interview was extremely valuable in identifying gaps in my knowledge and helping me understand where I need to improve.
★ Trustpilot
★★★★★I started my first program yesterday and was LOST on the first day. Sahid made sure that I regained my bearings and put me on the right track. I was surprised at the dedication and additional time he was willing to take. It is truly AH-MAZING to learn from instructors who are EXCITED about the content. So far, So GREAT!
★ Trustpilot
★★★★★I had a wonderful consultation session with Shahid. He took his time to explain my roadmap and proper structure to follow with realistic timelines. What I appreciated was how personalized the guidance felt - he understood exactly where I was in my journey and what I needed to do next. Keep the good work up!
★ Trustpilot
★★★★★I had a great experience with Sahid, Gracy and the team. They were very patient, knowledgeable, and took the time to address all my questions. The session helped clarify many of my doubts and gave me a much clearer understanding of the learning roadmap. As a working professional, I especially appreciated their practical guidance.
Rated 4.6/5 on Trustpilot · 267+ reviews
More learners who landed roles through K21 programs
Verified reviews from Google and Trustpilot. Every profile links to the real person on LinkedIn.
The hands-on projects let me confidently describe an end-to-end AI solution in the interview. The interviewer was genuinely impressed.
Worked hands-on with Azure AI services, Azure AI Foundry, and OpenAI. Later received a second offer as a Generative AI Engineer. K21 gave me the skills and confidence to choose.
As a VP of Software Engineering transitioning into AI/ML, I completed four courses and passed three Microsoft certification exams: AI-900, AZ-900, and AB-731. The hands-on projects on Azure gave me practical experience that actually shows up in interviews.
I was deploying a RAG pipeline to a web app during the actual hiring process. The projects I built at K21 were exactly what the company needed.
The Azure AI/ML trainee landed the job after completing the program and building out her portfolio.
The right training, courseware, job application support, and placement guidance to help students achieve their goals. I particularly like the quality and depth of the faculty's knowledge.
I am 3 weeks into the AI/ML program. The team creates a custom game plan and learning path for everyone. The real-world project experiences and hands-on lab experience is a key differentiator.
Connecting with Shahid was a turning point. He gave me clarity, practical guidance, and renewed confidence. The AI curriculum is exhaustive and serves as a one-stop shop for AI and ML.
Credits K21 Academy guidance for the preparation and is now moving on to the next Azure AI certification.
I joined K21 Academy and I'm really glad I did. The course is well-structured, catering to both beginners and advanced learners. The emphasis on hands-on experience truly enhances the learning process.
Credits the structured training and support system for making the transition into a cloud architecture role.
When I first started learning AI and Azure, I was quite confused about where to begin. K21's structured approach gave me direction and I now feel more confident in my learning journey.
Live interactive sessions · 1-year access · Unlimited retakes · All certifications covered in the curriculum.
Any 1 Track
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Any 2 Tracks
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All 3 Tracks
AWS + Azure + Agentic AI
Choose pay in full or instalments on each card.
Three steps, mapped to what AI engineering job descriptions ask for.
Infrastructure engineer today
The 20% you learn
12-18 weeks on average*
* 12-16 weeks for AWS and Azure, 12-18 weeks for Agentic AI. Timing varies by effort level, some complete faster.
No. You won't build apps from scratch, study data structures and algorithms, or need years of software engineering or a computer science degree. You will need basic Python (calling APIs, running scripts), AI frameworks like LangChain, connecting agents and tools together, and writing prompts and logic flows, and all of it is taught inside the program, on every track. Most people pick this up in 4 to 6 weeks.
Yes. The program is for engineers with experience in servers, networking, storage, VMs or containers, on-premise experience is enough. A cloud background is helpful but not required; cloud deployment is covered inside the program.
If you come from an AWS background, choose AWS AI/ML Engineer. If you work in Microsoft or enterprise environments, choose Azure AI ML Engineer. If you want the cutting edge and the most future-proof path, choose Agentic AI Engineer. Still unsure? Talk to a program advisor and we'll match you to the right one.
On average 12-16 weeks for the AWS and Azure tracks and 12-18 weeks for Agentic AI, based on part-time study. Timing varies by effort level, some learners complete faster.
Live interactive sessions, 1-year access, unlimited retakes, industry projects on your track and all of the track's certifications covered in the curriculum.
Yes. Any two tracks, AWS AI + Azure AI, AWS AI + Agentic AI, or Azure AI + Agentic AI, are $6,000 and all three tracks are $9,000.
No. You are becoming the person who makes AI work in production. Around 80% of the work, architecture, deployment, integration and troubleshooting, is what you already do. The 20% you learn is prompts, agent logic and basic Python.
Pick your path (AWS, Azure or Agentic AI) and become the person who makes AI work in production.
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