Online Trainer – Agentic AI, Machine Learning & Generative AI

Position Overview

The Online Trainer – Agentic AI, Machine Learning & Generative AI plays a key role in delivering engaging and impactful learning experiences to adult learners enrolled in our advanced AI and Agentic AI programmes.

The trainer will facilitate live online training sessions covering Agentic AI, AI Agents, Generative AI, Large Language Models, Machine Learning, Deep Learning, NLP, and AI-powered automation.

This role involves explaining complex AI concepts, demonstrating agentic AI architectures and workflows, conducting hands-on demonstrations using modern AI frameworks and tools, and connecting technical concepts with real-world business applications.

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

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

Key Responsibilities

Deliver live online instructor-led training sessions covering Agentic AI, Generative AI, Machine Learning, Deep Learning, NLP, and Large Language Models.

Cover core Agentic AI and Generative AI topics including:

• Agentic AI fundamentals and evolution
• AI Agents and autonomous AI systems
• Agent architectures and agentic workflows
• Large Language Models and LLM-powered applications
• Prompt Engineering and advanced prompting techniques
• 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
• AI workflow automation
• LLM orchestration
• Machine Learning and Deep Learning fundamentals
• Neural Networks, Attention Mechanisms, and Transformers
• Natural Language Processing and text classification
• BERT, GPT, GANs, VAEs, and Diffusion Models
• Responsible AI, security, bias, and ethical considerations
• Emerging trends in Agentic AI and autonomous AI systems

Prepare and continuously update:

• Training presentations
• Learning materials
• Practical exercises
• Hands-on activities
• Agentic AI demonstrations
• AI agent use cases
• Real-world business case studies
• Templates and reference materials

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

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

• LangChain
• LangGraph
• CrewAI
• AutoGen
• ChatGPT
• Hugging Face
• AI APIs and LLM platforms
• RAG frameworks and vector databases
• Workflow automation platforms

Guide learners through real-world Agentic AI scenarios using practical business and industry examples.

Demonstrate how AI agents can be designed to reason, plan, use tools, retrieve information, collaborate with other agents, and automate multi-step workflows.

Support learners in developing practical skills for building and implementing LLM-powered applications and Agentic AI solutions.

Maintain high learner engagement and instructional quality throughout all sessions.

Stay updated with current Agentic AI practices, Generative AI developments, LLM technologies, AI frameworks, autonomous AI systems, and emerging industry trends.

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

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Engineering, or a related field.
• 10+ years of professional experience in Artificial Intelligence, Machine Learning, Generative AI, Agentic AI, or related domains.
• Strong hands-on experience designing, developing, implementing, or deploying Agentic AI systems and LLM-powered applications.
• Prior experience delivering online instructor-led training for professionals or adult learners.
• Demonstrated expertise across AI, Machine Learning, Deep Learning, NLP, Generative AI, and Agentic AI.
• Strong understanding of:
• Agentic AI architectures and AI agents
• LLM-powered applications
• Agentic workflows and automation
• Large Language Models
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Tool calling and function calling
• AI agent memory and context management
• Planning, reasoning, and task decomposition
• Multi-agent systems
• LLM orchestration
• Machine Learning and Deep Learning
• Neural Networks, Attention Mechanisms, and Transformers
• NLP and text classification
• BERT, GPT, GANs, VAEs, and Diffusion Models
• Responsible AI, security, bias, and ethical AI
• Hands-on experience with Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks.
• Strong understanding of AI APIs, LLM integration, and AI automation workflows.
• Excellent communication, presentation, and facilitation skills.

Required Skills

• Strong hands-on expertise in Agentic AI and AI Agent development.
• Strong instructional and learner engagement abilities.
• Ability to simplify complex Agentic AI, AI/ML, and Generative AI concepts through practical examples and demonstrations.
• Experience building or demonstrating LLM-powered applications and AI agent workflows.
• Experience with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar Agentic AI frameworks.
• Strong understanding of RAG architectures, tool calling, function calling, and LLM orchestration.
• Ability to explain AI agent architectures, planning, reasoning, memory, and multi-agent collaboration.
• Strong understanding of Artificial Intelligence, Machine Learning, Deep Learning, NLP, and Generative AI.
• 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 use cases.

Preferred Skills

• Experience training professionals, working professionals, or adult learners in Agentic AI, AI/ML, or Generative AI.
• Experience creating Agentic AI training content, exercises, demonstrations, or case studies.
• Hands-on experience developing production-grade AI agents or agentic applications.
• Experience with multi-agent frameworks such as CrewAI, AutoGen, LangGraph, or similar technologies.
• Experience implementing RAG pipelines, vector databases, embeddings, and knowledge retrieval systems.
• Experience integrating LLMs with external APIs, databases, tools, and enterprise systems.
• Exposure to AI workflow automation and orchestration platforms.
• Experience with Python and AI development environments.
• Familiarity with emerging Agentic AI architectures, autonomous AI systems, and AI coding agents.
• Experience demonstrating AI agent use cases across different business functions.
• Strong understanding of responsible AI, AI ethics, security, bias, and governance.

Key Competencies

• Strong instructional and facilitation skills.
• Excellent Agentic AI, Generative AI, Machine Learning, and AI expertise.
• Strong hands-on understanding of AI agents and agentic architectures.
• Ability to explain complex technical concepts clearly using practical examples.
• Strong understanding of LLMs, RAG, tool calling, agent memory, planning, and multi-agent systems.
• Professional virtual presence.
• Analytical thinking and structured problem-solving.
• Strong learner engagement and mentoring mindset.
• Excellent communication and presentation abilities.
• Ability to demonstrate practical and business-focused Agentic AI applications.
• Passion for developing AI capabilities among professionals and learners.

Student Support & Mentorship

Provide individualized 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 Agentic AI, Generative AI, Machine Learning, and LLM concepts to practical business scenarios.

Help learners design and experiment with AI agents, agentic workflows, RAG applications, 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 Agentic AI and Generative AI skills.

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.

Work Schedule

Part-Time instructors typically work 8–12 hours per week, depending on training schedules.

Each training session is approximately 3 hours in duration.

Sessions are delivered live in a fully online format.

Flexibility for evening and weekend availability is preferred based on learner cohort schedules.