GenAI Engineer Trainer — Remote Part-Time

GIST Management Solutions

United States

Remote

USD 83,000 - 165,000

Part time

4 days ago

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Benefits offered by this job

Flexible schedule

Remote work

Job summary

GIST Management Solutions is seeking a part-time Generative AI Engineer Trainer to deliver live online instruction to USA-based students and professionals. You will teach LLM fundamentals, RAG, and agentic AI, guiding learners through hands-on labs and capstone projects using Python, LangChain, and cloud-based AI services.

The role emphasizes real-world labs, secure coding practices, and engaging virtual sessions aligned with US time zones.

Qualifications

4+ years of professional software engineering experience with 2+ years building production LLM-based systems.
Strong proficiency in Python for backend development and AI engineering.
Hands-on experience building LLM applications using LangChain, LangGraph or equivalent frameworks.
Experience with RAG, embeddings, vector databases, semantic search, and retrieval pipelines.
Practical experience building Agentic AI systems with tool calling, memory, state management, and multi-step workflows.
Familiarity with MCP, prompt engineering, context engineering, and AI security risks.
Experience with LLM evaluation, testing, observability, and tracing (LangSmith or similar).
Strong understanding of LLMs, NLP, transformers, and Generative AI architectures.
Experience with OpenAI, Anthropic, Bedrock, Azure OpenAI, Llama or comparable platforms.
Cloud experience, preferably Microsoft Azure, and cloud-native AI architectures.
Excellent spoken English and ability to present complex topics to a US audience.

Responsibilities

Deliver live, interactive online Generative AI training to USA-based learners aligned with US time zones.
Teach LLM fundamentals, Generative AI, Transformers, embeddings, NLP, foundation models, and modern AI architectures.
Train learners to build LLM applications using Python, LangChain, LangGraph, and related frameworks.
Teach RAG concepts including document processing, embeddings, vector databases, semantic search, and context optimization.
Instruct on Agentic AI topics: agents, tool calling, memory, state management, and orchestration.
Cover MCP, function/tool calling, and integration of AI agents with external APIs.
Discuss prompt engineering, context engineering, outputs, injection risks, and AI security best practices.
Guide learners in LLM evaluation and observability using datasets and LangSmith.
Provide capstone project mentorship from architecture to deployment and conduct mock interviews.