Agentic AI Trainer / Educator (Agentic Systems, LLMs, Automation)
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Key skills for this role
About the Role
Business Umbrella is building an Agentic AI Academy and seeks a lead educator to design and deliver training on building, deploying, and operating agentic AI systems.
Key Skills for This Role
Responsibilities
- Develop and deliver curricula on agentic AI fundamentals: agents, tools, memory, planning, orchestration, evaluation, and safety
- Run practical, project based workshops where participants implement agentic workflows using frameworks such as LangGraph, CrewAI, LangChain, and AutoGen
- Create hands on labs that require learners to integrate APIs, external tools, and data sources (RAG, CRMs, ATS, ticketing tools, etc.) into their agents
- Coach learners on prompt engineering for agents, tool design, memory strategies, and monitoring of agent runs (logs, traces, evaluation loops)
- Translate internal business processes into agentic workflows and turn them into capstone training projects mapped to real KPIs
- Continuously update all training content based on new patterns, frameworks, vendor capabilities, and lessons from live deployments
- Collaborate with AI engineers, data scientists, L&D, and business owners to align training with company strategy, compliance, and governance requirements
Requirements
- Proven experience building and deploying LLM or agent based applications in production or as advanced prototypes
- Strong grasp of agentic design patterns: planning, tool calling, memory, multi agent collaboration, and human in the loop supervision
- Proficiency with at least one agent framework (e.g., LangGraph, CrewAI, LangChain, AutoGen) and one major LLM provider (OpenAI, Anthropic, Google, etc.)
- Solid software/ML engineering fundamentals: Python, APIs, version control, testing, observability, basic data pipelines
- Experience teaching, mentoring, or building technical training/content for engineers, data professionals, or business power users
- Ability to simplify complex concepts into clear, stepwise learning paths tightly tied to business use cases
Full Job Posting
Role Overview
- Acts as the lead educator for building, deploying, and operating agentic AI systems (autonomous multi step, tool using agents) for internal teams and external clients.
- Designs end to end learning experiences (bootcamps, workshops, internal academies) that move learners from simple prompting to orchestrating agents that reason, plan, and act.
- Focuses on real business use cases such as recruitment automation, sales outreach agents, research copilots, customer support triage, and operations workflows.
Key Responsibilities
- Develop and deliver curricula on agentic AI fundamentals: agents, tools, memory, planning, orchestration, evaluation, and safety
- Run practical, project based workshops where participants implement agentic workflows using frameworks such as LangGraph, CrewAI, LangChain, and AutoGen
- Create hands on labs that require learners to integrate APIs, external tools, and data sources (RAG, CRMs, ATS, ticketing tools, etc.) into their agents
- Coach learners on prompt engineering for agents, tool design, memory strategies, and monitoring of agent runs (logs, traces, evaluation loops)
- Translate internal business processes into agentic workflows and turn them into capstone training projects mapped to real KPIs (time saved, throughput, quality)
- Continuously update all training content based on new patterns, frameworks, vendor capabilities, and lessons from live deployments
- Collaborate with AI engineers, data scientists, L&D, and business owners to align training with company strategy, compliance, and governance requirements
Required Skills & Experience
- Proven experience building and deploying LLM or agent based applications in production or as advanced prototypes (beyond simple chatbots)
- Strong grasp of agentic design patterns: planning, tool calling, memory, multi agent collaboration, and human in the loop supervision
- Proficiency with at least one agent framework (e.g., LangGraph, CrewAI, LangChain, AutoGen) and one major LLM provider (OpenAI, Anthropic, Google, etc.)
- Solid software/ML engineering fundamentals: Python, APIs, version control, testing, observability, basic data pipelines
- Experience teaching, mentoring, or building technical training/content for engineers, data professionals, or business power users
- Ability to simplify complex concepts into clear, stepwise learning paths tightly tied to business use cases
Preferred Qualifications
- Background in AI/ML training, L&D for tech teams, developer relations, or internal enablement for AI products
- Experience with evaluation frameworks, experimentation platforms, or reinforcement learning for autonomous agents
- Domain exposure to enterprise workflows such as recruitment, HR, CRM/sales, support, or operations, to design credible business scenarios
- Familiarity with MLOps, safety/guardrail tooling, prompt evaluation, and responsible AI practices
Success Metrics / Outcomes
- Learners can independently design, build, and deploy agentic workflows that achieve clear business outcomes (reduced manual effort, better conversion, faster cycle time)
- Time to production for agent based initiatives is significantly reduced after training
- High CSAT/NPS for training programs, strong attendance/engagement, and visible adoption of agentic best practices in real projects
- Clear improvement in measurable KPIs such as automation coverage, error reduction, or throughput for workflows converted to agentic AI
What We Offer
- Opportunity to build and lead the Agentic AI Academy for a portfolio of GCC clients
- Access to real world recruitment, sales, and operations data/workflows to design credible labs and capstones
- Flexible remote setup with collaboration across GCC and MENA teams
How to Apply
- Please apply with an updated CV or portfolio of AI/agentic projects, plus 1 2 examples (GitHub, slides, videos) of training or content you have created for technical or business audiences.
- Shortlisted candidates will be asked to run a 30 45 minute sample micro workshop on an agentic AI use case (e.g., recruitment agent, sales agent, research copilot).
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