Online Trainer – AGS & Agentic AI

Position Overview

The Online Trainer – AGS & Agentic AI will play a key role in delivering engaging, practical, and industry-focused learning experiences to adult learners enrolled in our advanced Generative AI and Agentic AI programmes.

The trainer will facilitate live online training sessions covering Advanced Generative AI, Agentic AI, AI Agents, Large Language Models, Prompt Engineering, RAG, AI-powered automation, multi-agent systems, AI workflows, and LLM-powered applications.

This role requires a strong combination of 8+ years of hands-on technical expertise, industry experience, and instructional ability. The trainer will be responsible for explaining advanced AI concepts, demonstrating real-world AI agent implementations, conducting hands-on activities, and connecting technical concepts to practical business use cases.

The ideal candidate should have significant hands-on experience designing, building, implementing, or deploying AI agents, agentic workflows, LLM-powered applications, RAG systems, tool-using agents, multi-agent systems, and AI automation solutions.

Candidates should also be comfortable working with modern AI tools and frameworks, including ChatGPT, Claude, Gemini, LangChain, LangGraph, CrewAI, AutoGen, AI APIs, coding agents, and workflow automation platforms.

Classes are delivered 100% online in a synchronous, instructor-led format.

Key Responsibilities

Deliver live online instructor-led training sessions covering Advanced Generative AI, Agentic AI, AI Agents, LLMs, AI automation, and emerging AI technologies.

Cover core AGS and Agentic AI topics including:

• Advanced Generative AI concepts and applications
• Evolution of Generative AI towards Agentic AI
• AI Agents and autonomous AI systems
• Agentic AI architectures and design patterns
• Agentic workflows and automation
• Large Language Models and LLM-powered applications
• Prompt Engineering and advanced prompting techniques
• Context Engineering
• Retrieval-Augmented Generation (RAG)
• Tool calling and function calling
• AI agent memory and context management
• Planning, reasoning, and task decomposition
• Multi-agent systems and agent collaboration
• LLM orchestration
• AI-powered workflow automation
• AI agents interacting with APIs, databases, files, and external tools
• Building end-to-end AI agent solutions
• Evaluating and improving AI agent performance
• Responsible AI, security, privacy, bias, and governance
• Emerging trends in Agentic AI and autonomous systems

Prepare and continuously update:

• Training presentations
• Learning materials
• Practical exercises
• Hands-on labs
• Agentic AI demonstrations
• AI agent use cases
• Real-world business case studies
• Prompt libraries and templates
• Reference architectures
• Practical assignments and activities

Conduct interactive workshops, Q&A sessions, knowledge checks, guided practice, and hands-on demonstrations.

Conduct practical demonstrations using modern AI tools, platforms, frameworks, and development environments such as:

• ChatGPT
• Claude
• Gemini
• Microsoft Copilot
• LangChain
• LangGraph
• CrewAI
• AutoGen
• AI APIs and LLM platforms
• RAG frameworks
• Vector databases
• Embedding and retrieval technologies
• n8n and other workflow automation platforms
• AI coding agents and development tools
• Python and AI development environments

Demonstrate how AI agents can:

• Understand and decompose complex tasks
• Plan and reason through multi-step problems
• Use tools and external APIs
• Retrieve and analyse information
• Maintain context and memory
• Collaborate with other specialised agents
• Execute automated workflows
• Interact with enterprise systems
• Generate and validate outputs
• Operate with human-in-the-loop oversight

Guide learners through real-world Agentic AI scenarios across business functions such as operations, marketing, finance, customer service, software development, research, productivity, and enterprise automation.

Support learners in designing and developing practical LLM-powered applications, AI agents, multi-agent systems, RAG applications, and AI automation workflows.

Maintain high learner engagement and instructional quality throughout all sessions.

Stay current with rapidly evolving Generative AI, Agentic AI, AI agent frameworks, LLMs, AI coding agents, automation platforms, and emerging AI architectures.

Collaborate with internal curriculum teams to continuously improve training content, practical activities, and learner outcomes.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 8+ years of professional experience in Artificial Intelligence, Generative AI, Machine Learning, Software Engineering, Data Science, or related technology domains.
• Significant hands-on experience with Generative AI and Agentic AI technologies.
• Demonstrated experience designing, developing, implementing, or deploying AI agents or LLM-powered applications.
• Prior experience delivering online instructor-led training to professionals or adult learners.
• Strong understanding of Generative AI, LLMs, Agentic AI, AI agents, automation, and AI-powered applications.
• Strong understanding of:
• Agentic AI architectures
• AI agent design and development
• LLM-powered applications
• Agentic workflows
• Prompt Engineering
• Context Engineering
• Retrieval-Augmented Generation (RAG)
• Tool calling and function calling
• Agent memory and context management
• Planning and reasoning
• Task decomposition
• Multi-agent systems
• LLM orchestration
• AI workflow automation
• AI APIs and integrations
• Responsible AI, security, privacy, and governance
• Hands-on experience with Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks.
• Strong experience working with modern LLM platforms such as OpenAI, Anthropic, Google Gemini, Microsoft Copilot, or similar platforms.
• Strong understanding of AI automation and workflow orchestration.
• Experience with Python or another programming language used for AI development.
• Excellent communication, presentation, storytelling, and facilitation skills.

Required Skills

• Strong hands-on expertise in Generative AI and Agentic AI.
• Strong practical experience building or implementing AI agents and agentic workflows.
• Ability to simplify advanced AI concepts through practical examples and live demonstrations.
• Experience building or demonstrating LLM-powered applications, RAG solutions, AI agents, and automation workflows.
• Experience with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar Agentic AI frameworks.
• Strong understanding of RAG, tool calling, function calling, agent memory, planning, reasoning, and LLM orchestration.
• Ability to explain multi-agent architectures and agent collaboration.
• Experience working with ChatGPT, Claude, Gemini, Microsoft Copilot, or similar AI assistants.
• Experience with AI APIs, integrations, external tools, databases, and enterprise systems.
• Experience with workflow automation platforms such as n8n or similar tools.
• Strong understanding of AI-powered business automation and productivity use cases.
• Excellent communication and storytelling skills.
• Experience conducting live virtual training using Zoom, Microsoft Teams, or similar platforms.
• Strong learner engagement, facilitation, and Q&A management skills.
• Ability to conduct interactive, activity-driven learning experiences.
• Ability to connect Agentic AI concepts with real-world business applications.

Preferred Skills

• Experience training senior professionals, technology professionals, or adult learners in Generative AI and Agentic AI.
• Experience creating Advanced Generative AI / Agentic AI training content, labs, exercises, demonstrations, and case studies.
• Hands-on experience developing production-grade AI agents or agentic applications.
• Experience implementing multi-agent systems using CrewAI, AutoGen, LangGraph, or similar technologies.
• Experience implementing RAG pipelines, vector databases, embeddings, and knowledge retrieval systems.
• Experience integrating LLMs with external APIs, databases, enterprise tools, and business applications.
• Experience with AI workflow automation and orchestration platforms, including n8n or similar platforms.
• Experience with AI coding agents such as Claude Code, Cursor, GitHub Copilot, Codex, or similar tools.
• Experience building AI-powered productivity and business automation solutions.
• Experience demonstrating AI agent use cases across different business functions.
• Familiarity with emerging Agentic AI architectures and autonomous AI systems.
• Strong understanding of responsible AI, AI security, privacy, ethics, governance, and risk management.

Student Support & Mentorship

Provide individualised learner support during live training sessions and scheduled office hours.

Maintain regular communication regarding learner progress, training expectations, and skill development.

Respond promptly and professionally to learner and internal team communications.

Provide timely, constructive feedback on practical exercises, activities, and learning assessments.

Support learners in applying Generative AI, Agentic AI, LLM, RAG, and AI automation concepts to practical business scenarios.

Help learners design and experiment with AI agents, multi-agent workflows, RAG applications, tool integrations, and AI-powered automation through guided practice and hands-on demonstrations.

Performance Monitoring

Evaluate learner progress based on participation, knowledge checks, practical exercises, and hands-on activities.

Maintain accurate records of learner engagement and performance.

Identify learners requiring additional support and collaborate with internal teams to improve learning outcomes.

Contribute to continuous improvement initiatives for training quality and learner success.

Collaboration & Professional Conduct

Adhere to institutional policies and instructional standards.

Foster an inclusive, collaborative, and professional learning environment.

Serve as a mentor and industry role model for learners developing their Generative AI and Agentic AI capabilities.

Collaborate with instructional staff and curriculum teams to enhance learner experience and training effectiveness.

Represent Simplilearn professionally when interacting with learners, staff, and external stakeholders.