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 Overview
The 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 Summary
We are seeking experienced AGS- 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 Responsibilities
Deliver live, instructor-led online classes
Conduct hands-on demos, guided practices, and projects
Explain concepts using real-world use cases
Address learner queries and ensure engagement Required Skills & Expertise
Generative AI & Foundation Models
Generative AI model types and applications
VAEs and GANs (architecture, use cases, limitations)
Transformer-based models and attention mechanisms
Self-attention and multi-head attention Language Models & LLMs
Language models fundamentals and applications
Large Language Models (architecture, training, operations, types) Retrieval-Augmented Generation (RAG)
RAG concepts, components, retrievers, and workflows
Real-world applications of RAG LangChain
LangChain architecture and core components
Building applications using LangChain
Prompt, memory, chains, and model integration
Text generation pipelines with Hugging Face models Prompt Engineering
Prompt fundamentals and optimization
Zero-shot, few-shot, CoT, Self-Consistency, ToT prompting
Prompt templates and LangChain prompts
Prompt engineering applications (data & synthetic data generation) Qualifications
8+ years of experience in Agentic Ai / Generative AI / NLP / LLMs
Bachelor’s degree in any field AND a minimum of 5+ years of professional experience in Artificial Intelligence or Machine Learning,
Strong proficiency in Python, LLM frameworks, and LangChain
Prior online or classroom training experience Requirements
Work Schedule
Part-Time instructors typically work 8–10 hours per week depending on cohort schedules.
Current AIML cohorts meet during evening hours:
Weekends- Saturday and Sunday
Sessions typically run 10:00 AM to 2 PM EST Benefits
Compensation
The 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 Opportunity
We 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 Authorization
Applicants must be legally authorized to work in the United States at the time of application and throughout employment.
