About this role
About: Simplilearn Simplilearn is the world’s #1 online Bootcamp provider, enabling learners around the globe with rigorous and highly specialized training offered in partnership with world-renowned universities and leading corporations. We focus on emerging technologies and skills, such as data science, cloud computing, programming, and more — that are transforming the global economy. Our training is hands-on and immersive, including live virtual classes, integrated labs and projects, 24x7 support, and a collaborative learning environment. Over two million professionals and 2000 corporate training organizations across 150 countries have harnessed our award-winning programs to achieve their career and business goals.
Simplilearn has collaborated with Full stack Academy to leverage its widespread footprint in the US region and partnerships with Top US universities to grow internationally
Position OverviewThe Part-Time Instructor for Artificial Intelligence and Machine Learning (AIML) plays a key role in delivering engaging and impactful learning experiences to adult learners enrolled in our online programs.
Instructors facilitate curriculum content, support student learning, and connect technical concepts to real-world industry applications. This role involves teaching live online sessions, mentoring students, providing feedback, and contributing to a collaborative instructional environment.
Classes are delivered 100% online in a synchronous format.
Job SummaryWe are seeking experienced Agentic AI Trainers to deliver live online training sessions covering modern generative models, LLMs, LangChain, RAG, and prompt engineering with hands-on demos and projects.
Key ResponsibilitiesDeliver live, instructor-led online classesConduct hands-on demos, guided practices, and projectsExplain concepts using real-world use casesAddress learner queries and ensure engagement Required Skills & ExpertiseGenerative AI & Foundation ModelsGenerative AI model types and applicationsVAEs and GANs (architecture, use cases, limitations)Transformer-based models and attention mechanismsSelf-attention and multi-head attention Language Models & LLMsLanguage models fundamentals and applicationsLarge Language Models (architecture, training, operations, types) Retrieval-Augmented Generation (RAG)RAG concepts, components, retrievers, and workflowsReal-world applications of RAG LangChainLangChain architecture and core componentsBuilding applications using LangChainPrompt, memory, chains, and model integrationText generation pipelines with Hugging Face models Prompt EngineeringPrompt fundamentals and optimizationZero-shot, few-shot, CoT, Self-Consistency, ToT promptingPrompt templates and LangChain promptsPrompt engineering applications (data & synthetic data generation) Model I/O & Data HandlingModel I/O (prompts, LLMs, output parsers)Document loaders (CSV, PDF, HTML, JSON, Markdown)Text splitters and embeddings in GenAI Qualifications8+ years of experience in Agentic Ai / Generative AI / NLP / LLMsStrong proficiency in Python, LLM frameworks, and LangChainPrior online or classroom training experience Requirements
Work SchedulePart-Time instructors typically work 8–10 hours per week depending on cohort schedules.
Current AIML cohorts meet during evening hours:
Weekends- Saturday and SundaySessions typically run 10:00 AM to 2 PM EST Benefits
CompensationThe anticipated pay range for this position is $60- per hour, depending on qualifications and experience.
This position is classified as Part-Time, Non-Exempt, and employees will be compensated for all hours worked in accordance with applicable federal and state wage and hour laws.
Equal Employment OpportunityWe are committed to creating an inclusive environment for all employees and applicants. Employment decisions are made without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.
Work AuthorizationApplicants must be legally authorized to work in the United States at the time of application and throughout employment.