Corporate Trainer - Agentic AI

Position:Corporate Trainer - Agentic AI

Type:Part-time

Mode:Remote (Online Training Delivery)

Experience Required:3-5 years (Mandatory) in AI/ML training and advanced technology curriculum development.

About the Role

We are seeking a visionary Corporate Trainer to architect and deliver a premier training program onAgentic AI. This role is designed for an expert who can bridge the gap between theoretical AI advancements and practical, strategic business implementation. You will equip professionals across functionsfrom executives to engineerswith the knowledge to harness autonomous AI agents, transforming organizational efficiency and innovation.

Key Responsibilities

• Curriculum Development & Customization:
• Design, develop, and continuously update a comprehensive, modular curriculum on Agentic AI (see detailedTable of Contentsbelow).
• Tailor content depth and focus to specific client needshigh-level strategy for leadership, technical deep-dives for developers, and use-case workshops for business units.
• Engaging Training Delivery:
• Facilitate dynamic, interactive online workshops and seminars that drive engagement and comprehension.
• Employ a blended learning approach: theory, live demos, real-world case studies, hands-on sandbox environments, and collaborative problem-solving sessions.
• Subject Matter Leadership:
• Serve as the go-to expert on the Agentic AI ecosystem, trends, tools, and ethical implications.
• Guide participants on strategic planning, implementation roadmaps, tool evaluation, and ROI measurement for agentic systems.
• Evaluation & Continuous Support:
• Create practical assessments, including scenario analyses and prototype-building projects, to gauge competency.
• Provide curated resources, office hours, and community forums to support learners beyond the core sessions.

Mandatory Qualifications & Experience

• 3-5 years of demonstrable experienceas a Corporate Trainer, Technical Evangelist, or Senior Learning Consultant specializing inArtificial Intelligence, Generative AI, or Intelligent Automation.
• Deep, hands-on understanding ofAgentic AI principles, architectures, and the current tooling landscape.
• Proven track record of designing and delivering successfulremote-first training programsfor corporate audiences.
• Exceptional ability to distill complex technical subjects into clear, compelling, and actionable learning journeys.
• High proficiency with virtual training tech stack (video conferencing, interactive whiteboards, LMS, etc.).

Comprehensive Curriculum Outline: Table of Contents

The successful candidate will be responsible for developing and teaching modules based on the following structure:

Module 1: Foundations of Agentic AI

• 1.1. From Automation to Autonomy: Defining the Agentic Shift
• 1.2. Core Components of an AI Agent: Perception, Planning, Action, Learning
• 1.3. Key Architectures: ReAct, Plan-and-Execute, Reflexion, Multi-Agent Systems
• 1.4. Distinction: Chatbots vs. Assistants vs. Autonomous Agents

Module 2: Building Blocks & Tooling Ecosystem

• 2.1. Critical Infrastructure: LLMs as the Brain, Memory (Vector, SQL), Tools & APIs
• 2.2. Orchestration Frameworks Deep Dive: LangGraph, AutoGen, CrewAI, DSPy
• 2.3. The Role of Platforms: AWS Bedrock Agents, Google Vertex AI Agent Builder, Microsoft Azure AI Agents
• 2.4. Selecting the Right Stack: A Decision Framework

Module 3: Designing Effective AI Agents

• 3.1. Prompt Engineering for Autonomy: Goal Decomposition, Self-Critique, Few-Shot Learning
• 3.2. Designing Agent Workflows: Sequential, Hierarchical, and Collaborative Patterns
• 3.3. Building for Reliability: Guardrails, Validation Layers, and Safety Protocols
• 3.4. Capstone Workshop: Storyboarding a Single-Agent Solution

Module 4: Multi-Agent Systems & Swarm Intelligence

• 4.1. Principles of Multi-Agent Collaboration: Specialization, Delegation, Debate
• 4.2. Communication Protocols: Shared Memory, Message Passing, Blackboard Systems
• 4.3. Managing Emergent Behavior: Coordination, Conflict Resolution, and Swarm Optimization
• 4.4. Case Study: Analyzing a Real-World Multi-Agent Implementation

Module 5: Development, Deployment & Lifecycle Management

• 5.1. Development Practices: Testing, Debugging, and Versioning for AI Agents
• 5.2. Deployment Strategies: Containerization, API Endpoints, and Scalability
• 5.3. Monitoring & Observability: Tracking Agent Performance, Cost, and Reasoning
• 5.4. The Feedback Loop: Continuous Learning and Agent Fine-tuning

Module 6: Strategic Implementation & Business Impact

• 6.1. Identifying High-ROI Use Cases Across Departments (Sales, Ops, R&D, HR)
• 6.2. Building the Business Case: Calculating Efficiency Gains and Value Creation
• 6.3. Change Management: Integrating Agents into Human-Centric Workflows
• 6.4. Risk Management & Ethics: Bias, Accountability, Transparency, and Control

Module 7: The Future Landscape & Capstone Project

• 7.1. Emerging Trends: Agent-Environment Interaction, Long-Horizon Planning
• 7.2. Organizational Readiness: Building an Agentic Culture and Skill Set
• 7.3.Final Capstone:Participants design and present a full Agentic AI solution blueprint for a chosen business problem.

Preferred Skills

• Direct hands-on experience building prototypes or applications with the frameworks mentioned (LangGraph, AutoGen, etc.).
• Previous background in software engineering, data science, or product management.
• A public presence (blog, talks, GitHub, LinkedIn content) demonstrating thought leadership in AI.
• Certifications in relevant cloud AI services or instructional design.