Get a $150K to $250K+ Job in Agentic AI & GenAI.
A hands-on program where you design, build, and deploy real Agentic AI systems — built for architects, developers/builders, and engineers who learn by building it, not by watching slides.
Agentic AI & GenAI Career Roadmap
4 levels. All for everyone.
4 levels. All for everyone.
Each one goes deeper.
All four layers are built for every learner — there is no optional path, no shortcut layer. Each layer builds directly on the last, taking you progressively deeper: from cloud and AI foundations, through Python for AI/ML, and all the way into production-grade Agentic AI & Generative AI Mastery. The depth is the point.
Level 1 — Common Foundation for Everyone
For Everyone
For Everyone
For Everyone
For Everyone
For Everyone
For Everyone
Cloud for Beginners
What is cloud computing and why it matters — AWS, Azure, Google & Oracle concepts — and 8 more core concepts.
Modules Covered
M00 Cloud for Beginners
▼- What is Cloud Computing and why it matters
- Public vs Private vs Hybrid Cloud
- IaaS, PaaS, SaaS — what each is good for
- Overview of AWS, Google Cloud, Oracle Cloud, Azure
- Core cloud concepts: regions, availability zones, scalability
- Cloud pricing models and cost basics
- Cloud security fundamentals
- ⚡ Lab: Explore free tiers across cloud providers
AWS for Beginners
Core AWS services, pricing, free tier, billing setup — EC2, S3, RDS, IAM, VPC — and 8+ core concepts.
Modules Covered
M01 AWS for Beginners
▼- What is Cloud Computing and why AWS
- EC2 — Compute: instance types, AMIs, pricing models
- S3 — Object Storage: buckets, storage classes, lifecycle
- RDS — Managed Databases: MySQL, PostgreSQL, Aurora basics
- IAM — Users, groups, roles, and permission policies
- VPC — Virtual Private Cloud, subnets, security groups
- AWS Pricing Calculator & Cost Explorer overview
- ⚡ Lab: Create an AWS Free Tier Account
- ⚡ Lab: Set Up Billing Alerts & Budget Controls
Google Cloud for Beginners
Core GCP services, $300 free trial, billing setup — Compute Engine, Cloud SQL, IAM, VPC — and 8+ core concepts.
Modules Covered
M02 Google Cloud Beginners
▼- What is Google Cloud and its global infrastructure
- Compute Engine — VM instances, machine families, pricing
- Cloud Storage — buckets, storage classes, data transfer
- Cloud SQL — Managed relational database on GCP
- IAM & Admin — roles, service accounts, permissions
- VPC Networks — subnets, firewall rules, routing
- Google Cloud Billing dashboard and cost export
- ⚡ Lab: Create a GCP Free Trial Account ($300 credit)
- ⚡ Lab: Set Up Budget Alerts & Cost Controls on GCP
Oracle Cloud for Beginners
Core OCI services, Always Free tier, billing setup — Compute, Autonomous DB, VCN — and 8+ core concepts.
Modules Covered
M03 Oracle Cloud Beginners
▼- What is Oracle Cloud Infrastructure (OCI)
- Compute Instances — shapes, VM and bare metal types
- Object Storage — buckets, tiers, lifecycle policies
- Autonomous Database — self-managing, self-patching DB
- IAM — compartments, users, groups, and policies
- VCN — Virtual Cloud Network, subnets, gateways
- OCI Cost Management dashboard and budget alerts
- ⚡ Lab: Create an Oracle Free Tier Account (Always Free)
- ⚡ Lab: Set Up Budget Alerts & Cost Tracking in OCI
AI, ML, GenAI & Agentic AI for Beginners
What is AI, Machine Learning, Deep Learning, GenAI and Agentic AI? — and 10+ more core concepts.
Modules Covered
M04 AI/ML/GenAI Beginners
▼- What is AI, Machine Learning, and Deep Learning?
- Generative AI and Large Language Models explained simply
- Agentic AI — what it is and how it differs from GenAI
- Real-world AI use cases across industries
- Introduction to OpenAI GPT, Claude, and Google Gemini
- How models learn: training, inference, and evaluation
- AI Safety and responsible AI basics
- ⚡ Lab: Create API Keys for OpenAI GPT and Claude
- ⚡ Lab: Build your first simple AI chatbot
Data for Beginners
Structured vs unstructured data, databases, data warehouses, and data lakes — and 8+ more core concepts.
Modules Covered
M05 Data for Beginners
▼- Structured vs unstructured data: what is the difference?
- Databases, data warehouses, and data lakes explained
- ETL basics: how data moves and gets transformed
- Cloud data services: SQL databases, blob/object storage
- Why good data is the foundation of good AI
- ⚡ Lab: Explore a SQL Database on Cloud
- ⚡ Lab: Visualize Data with a BI tool
- ⚡ Lab: Create Cloud Storage & Upload Data
↓
Level 2 — AI Certifications for Everyone
For Everyone
For Everyone
AWS AI Practitioner (AIF-C01)
Foundational AI/ML on AWS, Amazon Bedrock, generative AI, responsible AI — full exam prep included.
Modules Covered
AIF-C01 AWS AI Practitioner
▼- AI, ML, and Deep Learning fundamentals for AWS
- AWS AI/ML services: SageMaker, Bedrock, Rekognition, Polly, Lex
- Generative AI on AWS — Amazon Bedrock, Claude on AWS, Titan models
- Foundation Models, prompt engineering, and model customization
- Responsible AI, bias detection, and governance on AWS
- AIF-C01 exam structure, question types, and domain breakdown
- ⚡ Lab: AIF-C01 Full Mock Exam — Practice Test 1
- ⚡ Lab: AIF-C01 Full Mock Exam — Practice Test 2
Azure AI Fundamentals (AI-901)
AI workloads, ML principles, computer vision, NLP, GenAI workloads, Azure OpenAI — full exam prep.
Modules Covered
AI-901 Azure AI Fundamentals
▼- All AI workloads, ML principles, and responsible AI on Azure
- Computer vision and NLP workloads on Azure
- Generative AI workloads and Azure OpenAI Service
- Microsoft Foundry and Azure AI services overview
- Prompt engineering basics and Azure content safety
- Full AI-901 exam prep, question bank, and mock tests
- ⚡ Lab: AI-901 Full Mock Exam — Practice Test 1
- ⚡ Lab: AI-901 Full Mock Exam — Practice Test 2
↓
Level 3 — Python for AI/ML, GenAI & Agentic AI
Python 3
NumPy
Pandas
scikit-learn
Matplotlib
Jupyter
For Everyone
Introduction to Python for Machine Learning
Overview of Python — key features and benefits for AI/ML — and 11 more topics & labs.
Topics & Labs
Introduction to Python for ML
▼- Overview of Python — key features and benefits for AI/ML
- Setting up the Python environment (Anaconda, Jupyter, VSCode)
- Variables, data types, and type conversion
- Strings, lists, tuples — creation and manipulation
- Conditional statements and loops (for/while)
- Functions — defining, calling, arguments, return values
- Importing libraries and using pip
- ⚡ Lab: Introduction to Python for Machine Learning
For Everyone
Python Data Structures, Control Flow & Functions
Tuples — definition, use cases, hands-on — and 16 more topics & labs.
Topics & Labs
Python Data Structures
▼- Tuples — definition, use cases, hands-on
- Lists — indexing, slicing, list comprehensions
- Dictionaries — keys, values, nested dicts
- Sets — operations, membership testing
- Lambda functions, map, filter, reduce
- Exception handling — try/except/finally
- File I/O — reading and writing files in Python
- ⚡ Lab: Python Data Structures, Control Flow & Functions
For Everyone
Object-Oriented Programming (OOP)
OOP Part 1 — objects, classes, attributes, methods, __init__ — and 4 more topics & labs.
Topics & Labs
Object-Oriented Programming (OOP)
▼- OOP Part 1 — objects, classes, attributes, methods, __init__
- Inheritance, method overriding, super()
- Encapsulation — private, protected attributes
- Polymorphism — duck typing and method resolution order
- Decorators and class/static methods
- ⚡ Lab: Object-Oriented Programming (OOP)
For Everyone
Introducing Machine Learning in Detail
Machine Learning overview — types, workflow, real-world applications — and 12 more topics & labs.
Topics & Labs
Introducing Machine Learning
▼- Machine Learning overview — types, workflow, real-world applications
- NumPy — arrays, operations, broadcasting
- Pandas — DataFrames, indexing, groupby, merge
- Data cleaning — missing values, duplicates, outliers
- Exploratory Data Analysis (EDA) with Matplotlib and Seaborn
- Train/test split, cross-validation, and bias-variance tradeoff
- ⚡ Lab: Introducing Machine Learning in Detail
For Everyone
EDA & Feature Engineering
Feature engineering — encoding categorical variables, feature creation, binning, scaling — and 4 more topics & labs.
Topics & Labs
EDA & Feature Engineering
▼- Feature engineering — encoding categorical variables
- Feature creation, binning, and scaling (MinMax, Standard)
- Dimensionality reduction with PCA
- Correlation analysis and feature selection techniques
- Pipelines in scikit-learn for reproducible preprocessing
- ⚡ Lab: EDA & Feature Engineering
For Everyone
Supervised Machine Learning
Supervised ML overview — regression vs classification, labelled data — and 10 more topics & labs.
Topics & Labs
Supervised Machine Learning
▼- Supervised ML overview — regression vs classification
- Linear Regression — OLS, gradient descent, regularization
- Logistic Regression — sigmoid, decision boundary, metrics
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM) — kernels, margin
- Evaluation metrics — accuracy, F1, ROC-AUC, confusion matrix
- ⚡ Lab: Supervised Machine Learning
For Everyone
Ensemble Learning
Decision Trees — splitting criteria, Gini impurity, information gain, pruning — and 5 more topics & labs.
Topics & Labs
Ensemble Learning
▼- Decision Trees — splitting criteria, Gini impurity, information gain
- Bagging — Bootstrap aggregation, Random Forest
- Boosting — AdaBoost, Gradient Boosting, XGBoost
- Stacking and blending ensemble methods
- Hyperparameter tuning — GridSearchCV, RandomizedSearch
- ⚡ Lab: Ensemble Learning
For Everyone
Unsupervised Machine Learning
Unsupervised ML overview — clustering, dimensionality reduction, anomaly detection — and 9 more topics & labs.
Topics & Labs
Unsupervised Machine Learning
▼- Unsupervised ML overview — clustering, dimensionality reduction
- K-Means Clustering — algorithm, elbow method, silhouette score
- DBSCAN — density-based clustering
- Hierarchical Clustering and dendrograms
- Autoencoders for anomaly detection
- t-SNE and UMAP for high-dimensional visualization
- ⚡ Lab: Unsupervised Machine Learning
Certification
PCEP™ – Certified Entry-Level Python Programmer
The entry-level cert every AI/ML engineer starts with. Full exam prep included."
↓
Level 4 — Your Specialization
Open to All
Agentic AI & Generative AI Mastery
Design, build, and deploy production Agentic AI systems — from LLM foundations to multi-agent orchestration, guardrails, MCP, observability, and full AWS deployment. The complete production stack across 10 structured modules.
LangChainLangGraphOpenAI SDK
CrewAIAutoGenFastMCP
PresidioLangSmithLangfuse
FAISSChromaDBRAGAS
FastAPIDockerAWS EC2
10 Modules Covered
M01
GenAI & LLM Foundations
Transformer architecture, embeddings, vector spaces, and multi-provider LLM setup. Build a mini semantic-search engine and master structured outputs with Pydantic.
OpenAIGeminiClaudeGroqPydantic
⚡ 3 hands-on labs
▼
M02
Prompt Engineering
Zero-shot, few-shot, CoT, ReAct, Chain-of-Verification, plan-and-solve. Prompt caching with Anthropic & OpenAI — cost math, latency gains, what to cache.
ReActCoTCoVePrompt Caching
⚡ 3 hands-on labs
▼
M03
RAG System
End-to-end RAG pipeline with LangChain LCEL: loaders, chunking, embeddings, FAISS + ChromaDB. Hybrid BM25+vector retrieval, parent-child, multi-query, cross-encoder reranking.
LangChainFAISSChromaDBBM25
⚡ 5 hands-on labs
▼
M04
AI Agents Foundations & First ReAct Agent
What an agent is vs. a chatbot vs. a chain. Agent loop: think → act → observe → repeat. Tool-use fundamentals, LangChain @tool decorator, create_react_agent, and Agentic RAG.
LangChainReActTool UseAgentic RAG
⚡ 1 hands-on lab
▼
M05
LangGraph Core Workflow Patterns
StateGraph, TypedDict reducers, conditional edges, routing, parallelization (Send API fan-out/fan-in), Human-in-the-Loop interrupts, MemorySaver, and graph visualization.
LangGraphHITLMemorySaverSend API
⚡ 5 hands-on labs
▼
M06
Multi-Agent Architectures in LangGraph
Supervisor, Swarm, Orchestrator-Worker with dynamic planning, Evaluator-Optimizer loop, Reflection pattern. Supervisor-vs-Swarm decision framework.
SupervisorSwarmOrchestratorReflection
⚡ 2 hands-on labs
▼
M07
Multi-Framework Development + Voice Agents
OpenAI Agents SDK with handoffs & guardrails. Real-time Voice Agent with semantic VAD. CrewAI crews. AutoGen GroupChat. 4-framework comparison.
OpenAI SDKCrewAIAutoGenVoice
⚡ 3 hands-on labs
▼
M08
Context Engineering & 3-Layer Agent Evals
Context budgeting, JIT retrieval, compaction. 3-layer eval: LLM → RAG → Agent. Grader types: rule-based, LLM-as-Judge, trajectory. RAGAS metrics.
RAGASLLM-as-JudgeEval Harnesspass@k
⚡ 4 hands-on labs
▼
M09
Guardrails, Observability & MCP / A2A
4-layer guardrail stack: input sanitization → Presidio PII → output safety → HITL. LangSmith & Langfuse tracing. FastMCP server/client. A2A AgentCard protocol.
FastMCPA2ALangSmithLangfusePresidio
⚡ 3 hands-on labs
▼
M10
Production Deployment + Advanced Agents
Notebook → production workflow. FastAPI streaming endpoints. Chainlit & Streamlit UIs. Docker + nginx. Full AWS EC2 deployment — your capstone system goes live.
FastAPIDockerAWS EC2Chainlitnginx
⚡ 1 hands-on lab
▼
Your portfolio, on GitHub
7 production-grade Agentic AI projects.
Not toy scripts. Each one is built, deployed, and documented the way enterprise teams actually ship.
Project 01
Financial Analyst Agentic RAG
An autonomous financial advisor agent that retrieves documents, reasons over them using a ReAct loop, and returns cited recommendations.
Project 02
Customer Support Multi-Agent System
A production multi-agent customer support system. A Supervisor agent routes queries to specialist sub-agents (orders, returns, billing). Deployed to AWS EC2.
Project 03
Compliance Report Generator
An Orchestrator dynamically fans out work to specialized Worker agents, then runs an Evaluator-Optimizer loop to improve report quality before final output.
Project 04
CrewAI Content Production Pipeline
A multi-agent content crew: Researcher, Writer, Editor, and Publisher agents collaborate to produce platform-optimized fintech marketing content at scale.
Project 05
Secure Insurance Claims Agent + MCP
A secure claims pipeline with a full 4-layer guardrail stack (input sanitization, PII via Presidio, output safety, HITL approval). Exposes tools via MCP server.
Project 06
End-to-End Agentic RAG System
Production-grade Agentic RAG with hybrid BM25+vector retrieval, cross-encoder reranking, multi-query expansion, a RAGAS evaluation suite, and live FastAPI + Chainlit UI.
Project 07
Car Insurance Conversational AI Agent
A full-stack conversational agent handling policy queries, claim initiation, document collection, and escalation workflows. Integrates MCP, PII guardrails, and observability.
Bonus Module
AI Agent Security: 8 Pillars That Get You Past the Security Team.
The #1 technical question from 500+ enterprise practitioners at our live sessions. Eight pillars that separate a demo agent from one your security team will actually approve and deploy in production.
1
Prompt Injection Protection
Direct attacks + indirect XPIA (cross-prompt injection)
2
RBAC + Least Privilege
Scoped per agent, not per user
3
Managed Identities
No hardcoded credentials — ever
4
Guardrails + Content Filtering
Validate inputs and outputs in both directions
5
Network Security
Private endpoints, VNet injection, no public exposure
6
Audit Logging + Monitoring
Trace every agent action, end to end
7
Human-in-the-Loop Approval Gates
Pause before high-impact, irreversible actions
8
Red-Teaming with PyRIT
Attack your agent before attackers do
Free Webinar only Bonus Module Included
Enroll now →
From our learners
Real people. Real roles. Real results.
Verified outcomes from K21 Academy learners across the globe.
"
I'm thrilled to share that I've recently landed a role as a Generative AI Engineer. The hands-on projects let him confidently describe an end-to-end AI solution in the interview.
"
I've successfully landed a job as an AI Engineer. Worked hands-on with Azure AI services, Azure AI Foundry, and OpenAI. Later received a second offer as a Generative AI Engineer.
"
I've secured a position as an OpenShift Architect. Credits the structured training and support system for the transition.
"
I have finally secured a contract-to-hire role as a Generative AI Engineer. Was deploying a RAG pipeline to a web app during the hiring process.
"
I got cracked that role. The Azure AI/ML trainee landed the job after completing the program.
"
Excited to share that I have successfully passed the Microsoft AI-900 certification! Credits K21 Academy guidance, and is moving on to the next Azure AI certification.
What the program includes
Everything from skill-building to a job offer, in one system.
This is not a video library. It is a job-outcome system.
54 Hours Live Weekly Sessions
Interactive cohort, not recorded videos. Real-time Q&A with Agentic AI experts. Weekend live sessions with 24-hour recording access.
7 + 1 Capstone Projects on GitHub
Build a production-grade Agentic AI portfolio: RAG agents, multi-agent systems, MCP integrations, guardrail stacks, and more — all on GitHub.
3 AI Certifications
AWS AIF-C01, Azure AI-901 & Python (PCEP) cert prep built into the curriculum. Exam prep, practice tests, and guided revision included.
1-Year On-Job Support
Support continues after you're hired — through your first 90 days and beyond. We're invested in your outcome, not just your enrollment.
AI Agent Security Module — 8 Production Pillars
How do you actually secure a production AI agent? Eight pillars: prompt injection defense, RBAC, managed identities, guardrails, network isolation, audit logging, HITL gates, and red-teaming with PyRIT.
Choose your path
Upskill
$1,997
One Payment - Everything unlocked.
- ✓ 10 Modules - your 8 + 2 Surprise GenAI Bonus
- ✓ 25+ Hands-on Agentic AI labs
- ✓ 7 + 1 Project Portfolio on GitHub
- ✓ 500+ Practice Questions
- ✓ 3 Cert Prep (AWS AIF-C01, Azure AI-901 & Python (PCEP))
- ✓Weekly LIVE sessions, Mentor access & Community
- 🔒 AI Agent Security Bonus — 8 pillars (72-hr window)
Job-Prep
Everything in the Upskilling program — 100% included, plus the full job-landing system.
- ✓ Everything in the Upskilling program — 100% included
- ✓ 7 + 1 Agentic AI project portfolio on GitHub
- ✓ Resume + LinkedIn rebuild for Agentic AI roles
- ✓ Unlimited mock interviews with hiring managers — 1-year window
- ✓ 1 month on-job support after you land the role
- 🔒 AI Agent Security Bonus — 8 pillars (72-hr window)
The guarantee — 6 months. Love it or leave it.
Your decision is protected. Do the work, and if it doesn't deliver, you get your money back.
✓ Complete all hands-on labs and projects
✓ Apply to a minimum of 50 Agentic AI–relevant roles
✓ Get your resume reviewed by K21's team
✓ Ask for support when you need it — don't go silent
Did all that and still not satisfied? Full refund. Action-based, six months, no fine print.
Common questions
Before you enroll.
YouTube is free. Why pay?
YouTube gives you fragments. This program gives you a structured, progressive system — modules that build on each other, live weekly sessions where you actually build alongside experts, 7 capstone projects you can show employers, resume optimization, mock interviews, and 12 months of support after you're hired.
I already know GenAI. I've built a few chatbots.
Good — that means you can skip the early foundation and move faster. But building chatbots is not the same as building production Agentic AI systems. This program covers multi-agent orchestration, LangGraph workflow patterns, 5 frameworks, guardrail stacks, eval methodologies, MCP/A2A protocol, and Docker + EC2 deployment.
I don't have time. I'm working full-time.
The live sessions run on weekends, with 24-hour recording access. Most learners complete the labs in focused 2–3 hour blocks during evenings or weekends. The curriculum is structured specifically for working professionals.
How long is the program?
The core program is delivered over approximately 12–16 weeks of live sessions (54 hours total). Most learners complete projects and job prep activities over 4–6 months. On-job support continues for 12 months from your enrollment date.
What job roles can I apply for after completing the program?
Agentic AI Engineer, AI Agent Architect, AgentOps / AI Ops Engineer, AI Solutions Architect, AI Agent Developer, GenAI Platform Engineer, LLM Application Engineer, and AI Product Manager roles.
Do you offer a money-back guarantee?
Yes — a full 6-month action-based guarantee. Complete the labs and projects, apply to 50+ relevant roles, get your resume reviewed, and ask for support when needed. If you've done all of that and you're still not satisfied, you get a full refund. No fine print.
Is your training live or recorded?
Live. Every session is a real-time interactive cohort. You can ask questions, debug together, and get feedback on your code. Recordings are available within 24 hours.
What tools and frameworks will I learn?
LangChain, LangGraph, OpenAI Agents SDK, CrewAI, AutoGen/AG2, FastMCP, Microsoft Presidio, LangSmith, Langfuse, FAISS, ChromaDB, RAGAS, FastAPI, Chainlit, Streamlit, Docker, nginx, and AWS EC2. The full Agentic AI production stack.
How many projects will I build?
7 capstone projects — one per major architecture pattern. Each is built, deployed, and pushed to GitHub. You'll also build smaller lab projects throughout each module.
Ready to land your Agentic AI role?
Join the next cohort and build the skills, portfolio, and support system that gets you hired.
View pricing & enroll →
