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Hybrid Program (Live Mentorship + Self-paced Learning)

Agentic AI Engineer Certification Training

Elevate your AI skills with our Azure AI Engineer Course and Training. Learn to build, deploy, and manage enterprise-grade AI applications on Azure unified platform. Ideal for developers and data professionals seeking hands-on experience.

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12 Live Sessions
11 Notes
20 Hands-on Labs
6 Skill Tests
106 Q&A Guides

Tools and Technologies Covered

Azure Services
Microsoft Foundry
AWS Services
Bedrock
Bedrock AgentCore
LangGraph
Microsoft Agent
LLMs
LLama Model
GPT
Phi Model
AI Agent
MCP Server

Agentic AI Engineer Certification Training Key Features

6 Weeks of Intensive Live Training

Interactive sessions with real-time problem solving

Learn from Microsoft MVPs

Training by globally recognized experts

Build Food Delivery Website

Hands-on project for your portfolio

Career Coaching

Personalized career guidance from mentors

Resume & Portfolio Building

Expert review and portfolio enhancement

Job Assistance

Placement support with top companies

Why Choose Our Agentic AI Engineer Training Course?

    1.) High Demand: Over 80% of enterprises now adopt Azure-powered AI solutions (Gartner, 2024). With Azure capturing 30% of the cloud AI market, certified professionals and hands-on project expertise—are in high demand globally.

    2.) Attractive Compensation: Azure Gen AI Engineers enjoy competitive salaries:

    • US: $100,000 - $150,000 per year.
    • India: INR 15,00,000 - INR 30,00,000 annually.

    3.) Career Growth Opportunities: With the Azure Agentic AI Engineer Certification, you open the door to various career paths like Machine Learning Engineer, Data Scientist, and AI Solutions Architect in leading companies such as Microsoft, Google, and Amazon.

    4.) Work on Cutting-Edge Technologies: As AI and machine learning evolve, Azure Gen AI Engineers work with the latest tools and technologies to innovate in industries like healthcare, finance, and e-commerce.

    5.) Future-Proof Your Career: The global AI market will hit $407B by 2027. Azure AI roles are growing 35% YoY, offering unmatched job stability and career growth.

    No.1

    Public Cloud Provider

    ~10M

    Websites use Azure/AWS

    200+

    Cloud services offered

    INR 35-55 LPA

    Agentic AI Engineer Salary

    140+

    Countries availability

    ~28.7%

    Developers use Azure/AWS

    Agentic AI Engineer Career Scope

    Azure Agentic AI Engineer
    Gen AI Architect
    AWS Agentic AI Engineer
    Agentic AI Engineer
    LLM Engineer
    ₹25 LPA
    Avg package
    35%
    Avg hike
    3,200+
    Transitions
    78%
    Demand
    Annual Salaries (₹)
    ₹55 LPA
    Avg package
    40%
    Avg hike
    3,200+
    Transitions
    75%
    Demand
    Annual Salaries (₹)
    ₹52 LPA
    Avg package
    38%
    Avg hike
    3,200+
    Transitions
    80%
    Demand
    Annual Salaries (₹)
    ₹27 LPA
    Avg package
    35%
    Avg hike
    3,200+
    Transitions
    72%
    Demand
    Annual Salaries (₹)
    ₹32 LPA
    Avg package
    80%
    Avg hike
    6,000+
    Transitions
    99%
    Demand
    Annual Salaries (₹)

    Course Curriculum

    Agentic AI Foundations

    Setting Up Azure AI Foundry

    1. Introduction to Azure AI Foundry
    2. Azure AI Foundry Models
    3. Environment Setup: VS Code with AI extensions, Docker Desktop prerequisites

    Azure AI Foundry Fundamentals

    1. Strategic Role: How Foundry powers Microsoft’s generative AI stack
    2. Core Components: Projects, hubs, model catalog, SDKs
    3. Workspace Management: Projects, hubs, workspaces, and access controls
    4. Enterprise Readiness: Governance, compliance, and RBAC/identity integration
    Models & Hybrid AI

    Foundation Models in Azure AI Foundry

    1. Catalog Exploration: GPT, Grok, DeepSeek, Phi, Hugging Face, LLaMA
    2. Model Selection: Latency, cost, performance, and domain alignment
    3. Deployment Options
    4. Lifecycle Management: Testing, evaluation, versioning, and optimization

    Hybrid AI with Docker & OSS Models

    1. Local AI Setup: Running LLaMA, Mistral, Phi, Hugging Face Transformers in Docker
    2. Hybrid Workflows: Combining Azure-hosted models with OSS models
    3. Enterprise Patterns: Cost-performance tradeoffs and security for hybrid deployments
    Intelligent Agents and Orchestration

    Building AI Applications

    1. SDKs & Templates: Deep dive into Azure AI Foundry development stack
    2. Prompt Flow: Designing and iterating LLM applications
    3. LangChain & Semantic Memory
    4. API Extensions: External API calls, streaming, error handling

    Enterprise Copilot Development

    1. Copilot Design: Task-specific architecture and workflows
    2. RAG at Scale: Vector databases (Azure AI Search)
    3. Document Ingestion Pipelines: Data prep and embedding strategies
    4. Performance Optimization: Latency reduction, context management, UX best practices
    Multi Agents and MCP Servers

    Multi-Agent Systems

    1. Agents 101: Roles, collaboration models, and 2025 trends
    2. Introducing Microsoft Agent Framework
    3. Working with Microsoft Agent Framework
    4. Error Handling: Recovery flows and validation agents
    5. Build a multi agent workflow

    Model Context Protocol (MCP) Integrations

    1. MCP Architecture: Servers, clients, and communication patterns
    2. Enterprise Data Integration: SQL Server, Blob Storage, Cosmos DB or SharePoint
    3. Security & Performance: Authentication, caching, and scaling MCP services
    4. MCP-enabled workflow with enterprise system integration
    Responsible AI & DevOps

    Responsible AI & Monitoring

    1. Ethical AI: Content safety, bias mitigation, compliance frameworks
    2. LLMOps in Action: Prompt evaluation, hallucination detection, groundedness
    3. Monitoring: App Insights integration
    4. Evaluate & monitor a RAG-enabled enterprise copilot

    Integration & DevOps for AI

    1. CI/CD Pipelines: Azure DevOps, GitHub Actions
    2. Automated Testing: Validation, regression testing, blue-green deployments
    3. Enterprise Integration: Teams, Power BI
    4. Governance & Ops: Cost optimization, policy enforcement, DR/business continuity
    DOWNLOAD DETAILED CURRICULUM
    1. Architectural Overview of Azure AI Foundry
    0:07:30
    2. Introduction to Azure AI Foundry
    0:11:00
    3. Identity and Access Management in Azure AI Foundry
    0:10:00
    4. Using the Azure AI Foundry SDK and Command-Line Tools
    0:07:00
    5. Automating AI Workflows Using Prompt Flow and Pipelines
    0:08:30
    6. Responsible AI and Compliance Features in Foundry
    0:06:30
    7. Adoption of Generative AI Through Azure AI Foundry
    0:08:00
    8. Observability and Performance Monitoring Tools in Azure AI Foundry
    0:13:00
    9. Features and Capabilities of Azure AI Studio
    0:08:30
    10. Exploring the AI Model Catalog in Azure AI Foundry
    0:08:00
    11. Implementing CI/CD Pipelines for AI Model Deployment
    0:13:00
    1. Azure AI Foundry: Generative AI Fundamentals with Azure OpenAI
    00:30:00
    2. Azure AI Foundry: Responsible AI with Azure OpenAI
    00:35:00
    3. Azure AI Foundry: Prompting with Azure AI
    00:30:00
    4. Azure AI Foundry: Exploring and Comparing Different LLMs
    00:45:00
    5. Azure AI Foundry: Generate Images with Azure DALL·E in Python
    00:50:00
    6. Azure AI Foundry: Building Layered AI Prompts
    00:45:00
    7. Azure AI Foundry: Building Chat Applications
    00:45:00
    8. Azure AI Foundry: Text Generation Apps
    00:30:00
    9. Azure AI Foundry: Mini Search Engine with Azure AI & Python
    00:45:00
    10. Azure AI Foundry: AI Weather App with Function Calling
    00:50:00
    11. Azure AI Foundry: AI Chat App with Azure OpenAI & Flask
    00:30:00
    12. Azure AI Foundry: Conversational App with Flask & OpenAI
    00:40:00
    13. Azure AI Foundry: Generative AI Application Lifecycle
    00:50:00
    14. Azure AI Foundry: RAG AI Chat App with Azure & Flask
    00:45:00
    15. Azure AI Foundry: AI Policy Reviewer with Flask & Azure
    00:50:00
    16. Azure AI Foundry: AI Policy Reviewer with Azure Agents
    00:55:00
    17. Azure AI Foundry: SLM App with Azure OpenAI & Flask
    00:40:00
    18. Azure AI Foundry: Fine-Tuned AI Assistant with Azure & Flask
    00:45:00
    19. Azure AI Foundry: Low-Code AI App with Azure & Flask
    00:40:00
    20. Azure AI Foundry: LLaMA Chat App with Hugging Face & Flask
    00:50:00
    1. Introduction to Azure AI Foundry
    15 Questions
    2. Azure AI Foundry Architecture and Core Services
    15 Questions
    3. Azure AI Foundry Data Management and Integration
    15 Questions
    4. Azure AI Foundry Model Deployment and Monitoring
    15 Questions
    5. Azure AI Foundry Security, Networking, and Governance
    15 Questions
    6. Azure AI Foundry Developer Tools and Ecosystem Integration
    15 Questions

    Q&A Guides

    Introduction to Azure AI Foundry
    0:19:00
    Azure Generative AI Overview & Core Concepts
    0:20:00
    Core Advantages of Using Azure AI Foundry
    0:20:00
    Introduction to Azure AI Foundry Platform
    0:18:00
    Adoption of Generative AI Through Azure AI Foundry
    0:16:00
    Fundamentals of AI Agents
    0:15:00
    Key User Personas Supported by Azure AI Foundry
    0:18:00
    Types of AI Agents and Comparison with Chatbots
    0:19:00
    Integration of Azure AI Foundry with Microsoft AI Services
    0:19:00
    Prompt Engineering Fundamentals
    0:16:00
    Prompt Engineering Fundamentals
    0:16:00
    Intelligent Agent Fundamentals
    0:19:00

    Introduction to Artificial Intelligence & Machine Learning
    0:20:00
    Evolution to Agentic AI & Large Language Models (LLMs)
    0:17:00
    Hubs, Projects & Workspaces Architecture
    0:15:00
    Architectural Overview of Azure AI Foundry
    0:16:00
    Azure Model Catalog for Gen AI
    0:16:00
    Chain-of-Thought Reasoning
    0:18:00
    Features and Capabilities of Azure AI Studio
    0:18:00
    Smart Model Selection for Modern AI Workloads
    0:20:00
    Role and Functionality of Azure AI Agent Service
    0:17:00
    Exploring the AI Model Catalog in Azure AI Foundry
    00:16:00
    Components of Agent Architecture
    0:15:00
    Customization and Fine-Tuning of AI Models
    00:19:00
    Perception–Reasoning–Action Loop
    0:19:00
    Planning and Task Decomposition
    0:16:00
    Advanced Prompting (Layered Techniques)
    0:20:00
    Action Execution Mechanisms
    0:18:00
    Short-Term Memory in Agents
    0:20:00
    Long-Term Memory in Agents
    0:17:00
    RAG Architecture & Core Principles
    0:17:00
    Learning and Adaptation
    0:19:00
    Embedding Strategies & Models
    0:18:00
    Multi-Agent Collaboration Models
    0:18:00
    MCP Architecture
    0:20:00
    Single-Agent vs Multi-Agent Systems
    0:16:00
    Multi-Agent Collaboration
    0:18:00
    Use Cases of Azure AI Foundry in Key Industries
    00:16:00
    Role-Based Agent Design
    0:20:00
    Building Enterprise-Grade AI Assistants and Agents
    00:17:00
    Agent Communication Mechanisms
    0:17:00
    Multi-Agent Orchestration with Azure AI Foundry
    00:19:00
    Case Studies and Customer Success Stories
    00:16:00
    Future Trends and Roadmap for Azure AI Foundry
    00:20:00
    Agent Design Patterns
    0:16:00

    Data Ingestion Pipelines in Azure AI Foundry
    00:18:00
    Supported Data Sources and Formats
    00:19:00
    Using Azure Blob Storage and Data Lake for AI Workloads
    00:17:00
    Dataset Registration and Version Control
    00:19:00
    Integration with Azure Cosmos DB and SQL Databases
    00:20:00
    Context Management Techniques
    0:15:00
    Document Ingestion & Preprocessing
    0:16:00
    Introduction to RAG
    0:16:00
    Embeddings and Vector Databases
    0:18:00
    Vector Databases (Azure Search, Pinecone, Redis)
    0:20:00
    Semantic Search Techniques
    0:20:00
    Context Injection Strategies
    0:17:00
    Enterprise Data Integration
    0:16:00

    Deploying Models: Cloud, Edge & Hybrid
    0:18:00
    Handling Hallucination and Errors
    0:17:00
    Model Evaluation, Testing, Tuning & Versioning
    0:15:00
    Model Deployment Options in Azure AI Foundry
    00:18:00
    Deploying Models to Containers and Managed Endpoints
    00:17:00
    Observability and Performance Monitoring Tools
    00:19:00
    Logging, Alerts, and Metrics with Prometheus and Grafana
    00:18:00
    Troubleshooting and Debugging AI Workloads
    00:18:00
    Latency, Ranking & Optimization
    0:15:00
    Observability, Monitoring & Telemetry
    0:18:00
    Agent Development Lifecycle
    0:15:00
    Testing and Evaluation of Agents
    0:20:00
    Deployment Strategies (Cloud/Web/API)
    0:16:00
    Monitoring and Optimization
    0:19:00
    Performance Optimization and Cost Management in Agents
    0:20:00

    Identity, Access Control & Responsible AI Essentials
    0:20:00
    Error Handling, Guardrails & Quality Controls
    0:19:00
    Identity and Access Management in Azure AI Foundry
    00:19:00
    Implementing Private Networking and Endpoint Protection
    00:18:00
    Securing AI Data and Content Filtering Capabilities
    00:16:00
    Applying Azure Policy and Resource Locks for Governance
    00:19:00
    Responsible AI and Compliance Features in Foundry
    00:20:00
    Validation Agents & Error Recovery
    0:15:00
    Scaling, Authentication & Secure Context Sharing
    0:19:00
    Responsible AI Policies & Compliance
    0:20:00
    Responsible AI and Ethics
    0:15:00
    Security and Privacy in Agents
    0:18:00

    Azure AI Studio Features
    0:17:00
    Tool Calling and Function Execution
    0:20:00
    Running Local LLMs with Docker & Transformers
    0:20:00
    Building LLM Workflows with Prompt Flow
    0:18:00
    Function Calling, Tools & External APIs
    0:16:00
    LangChain for Agent Development
    0:15:00
    Microsoft Agent Framework
    0:20:00
    LangGraph for Agent Workflow Orchestration
    0:19:00
    Copilot & Agent-Orchestrated Workflows
    0:17:00
    Microsoft Agent Framework
    0:16:00
    Microsoft Foundry Agents
    0:18:00
    CrewAI Framework
    0:20:00
    Using the Azure AI Foundry SDK and Command-Line Tools
    00:17:00
    API Integration and Tool Usage
    0:17:00
    Integrating GitHub and Azure DevOps with AI Projects
    00:20:00
    Building MCP Services
    0:19:00
    AWS Agent Services
    0:15:00
    Automating AI Workflows Using Prompt Flow and Pipelines
    00:19:00
    Multi-Cloud Agent Integration Strategies
    0:19:00
    Leveraging Copilot Studio to Build AI Applications
    00:18:00
    Implementing CI/CD Pipelines for AI Model Deployment
    00:19:00

    OUR ALUMNI WORK AT

    Trusted by learners whose careers now thrive at leading companies

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    Live, expert-led classes mapped to your certification.

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    Live Sessions 12
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    Live Sessions 12
    Labs 20+
    Q&A 106+
    Notes 11+
    Skill Tests 6
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    Course Mentors

    Shailendra Chauhan
    10X MICROSOFT MVP AI ARCHITECT

    Shailendra Chauhan

    Microsoft MVP, Founder & CEO at ScholarHat

    17+ Years of Industry Experience as Mentor & Solution Architect
    Expert in .NET, Angular, React & Python
    Azure Cloud & AI/ML/Gen AI Specialist
    Bhawna Gunwani

    Bhawna Gunwani

    Corporate Trainer

    15+ Years Technical & Corporate Training
    Expert in Microsoft Tech, React, Angular & Node
    Global Training: TCS, Infosys, Accenture & More
    Rahul Kumar

    Rahul Kumar

    Author and .NET Tech Lead

    16+ Years of Industry Experience as .NET Mentor & Tech Lead
    Mastery in .NET Development Practices
    Lifelong Innovator & Team Inspirer
    Shailendra Chauhan
    10X MICROSOFT MVP AI ARCHITECT

    Shailendra Chauhan

    Microsoft MVP, Founder & CEO at ScholarHat

    17+ Years of Industry Experience as Mentor & Solution Architect
    Expert in .NET, Angular, React & Python
    Azure Cloud & AI/ML/Gen AI Specialist
    View Profile
    Bhawna Gunwani

    Bhawna Gunwani

    Corporate Trainer

    15+ Years Technical & Corporate Training
    Expert in Microsoft Tech, React, Angular & Node
    Global Training: TCS, Infosys, Accenture & More
    View Profile
    Rahul Kumar

    Rahul Kumar

    Author and .NET Tech Lead

    16+ Years of Industry Experience as .NET Mentor & Tech Lead
    Mastery in .NET Development Practices
    Lifelong Innovator & Team Inspirer
    View Profile

    Frequently Asked Questions

    Q1. What is the Azure AI Foundry Course?
    The Azure AI Foundry Course is a hands-on training program designed to help you master building, deploying, and managing AI-powered applications and agents using Microsoft’s unified Azure AI Foundry platform. You’ll learn to use cutting-edge tools, models, and workflows to create intelligent solutions for real-world business needs
    Q2. Is an Azure AI Engineer certificate worth it?
    Absolutely! If you're aiming for a career in AI, machine learning, or data science—especially within the Microsoft ecosystem—the Azure AI Engineer (AI-102) certification can give you a real edge. It proves your ability to build, manage, and deploy AI solutions using Azure tools. Plus, it’s valued by employers looking for cloud-savvy AI professionals.
    Q3. How long does the Azure AI Foundry certification process take?
    It usually depends on your background, but on average, the Azure AI Foundry training and certification path takes 4 to 6 weeks if you’re studying part-time. If you’re already familiar with Python and Azure basics, you might move faster.
    Q4. What is the cost of the Azure AI Foundry training program?
    The price can vary depending on the training provider, but most Azure AI Foundry programs cost between $500 and $1,200 USD. Some platforms also offer financial aid or discounts for students and early registrants.
    Q5. Are there any hands-on labs included in the Azure AI Foundry course?
    Yes, hands-on labs are a big part of the experience! The course is designed to be practical, so you’ll get access to real Azure environments to practice building chatbots, computer vision apps, and natural language processing models.
    Q6. Do I need AI-900 before AI-102?
    Not at all, AI-900 (Azure AI Fundamentals) is optional. It’s great for beginners and can help you build a solid foundation, but if you're already comfortable with basic AI concepts and Azure, you can go straight to AI-102.
    Q7. Who is eligible for AI-102?
    Anyone interested in building AI solutions on Azure can take AI-102! That said, it’s best suited for developers, data scientists, and engineers with some experience in Python and a basic understanding of Azure services. If you’ve worked on AI models or applications before—even better!
    Q8. Do I get access to hands-on labs and real Azure environments?
    Yes! Most AI-102 training programs, including Azure AI Foundry, provide access to live labs, sandbox environments, and real-world projects. These help you apply what you learn in a practical, job-ready way.
    Q1. Can I Attend a Demo Session before Enrolment?
    Yes, you can Attend a Demo Session before Enrolment in angular certification course. It gives you the opportunity to assess whether the training program aligns with your learning objectives. So, don't hesitate! Take advantage of this opportunity and attend a demo session before making your decision.
    Q2. Can I request for a support session if I need to better understand the topics?
    Yes, of course you can request for a support session if you need to better understand the topics. For that, you need to be in touch with the counsellor. Contact on +91- 999 9123 502 or you can mail us at hello@scholarhat.com
    Q3. Who are your mentors?
    All our mentors are highly qualified and experience professionals. All have at least 8-10 yrs of development experience in various technologies and are trained by ScholarHat to deliver interactive training to the participants.
    Q4. What If I miss my online training class?
    All online training classes are recorded. You will get the recorded sessions so that you can watch the online classes when you want. Also, you can join other class to do your missing classes.
    Q5. Can I share my course with someone else?
    In short, no. Check our licensing that you agree to by using ScholarHat LMS. We track this stuff, any abuse of copyright is taken seriously. Thanks for your understanding on this one.
    Q6. Do you provide any course material or live session videos?
    Yes we do. You will get access to the entire content including class videos, mockups, and assignments through LMS.
    Q7. Do you provide training on latest technology version?
    Yes we do. As the technology upgrades we do update our content and provide your training on latest version of that technology.
    Q8. Do you prepare me for the job interview?
    Yes, we do. We will discuss all possible technical interview questions and answers during the training program so that you can prepare yourself for interview.
    Q9. Will I get placement assistance after receiving my course completion certificate?
    Yes, you’ll get placement assistance after receiving your course completion certificate. The placement assistance provided by the US will guide you through the job search process, help you polish your resume, and connect you with potential employers. For that, you need to be in touch with the counsellor. Contact on +91- 999 9123 502 or you can mail us at hello@scholarhat.com
    Still have some questions? Let's discuss.
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