Generative AI Instructor

Job Description Company Description Since 2020, AlmaBetter has been a pioneer in online technical education, specializing in Data Science and Web Development. With a community of over 50,000 learners and 2000+ successful placements, we bridge the skill gap and empower the tech workforce for a better tomorrow. Gain access to industry professionals from top companies like LinkedIn, Google, Microsoft, Netflix, and Airbnb. With live classes, coding problems, mock interviews, real-world projects, and a pay-after-placement program, we offer a practical and immersive learning experience. Choose AlmaBetter as your trusted partner for tech education and excel in the fast-paced tech industry. Role Overview: We are looking for a passionate GenAI Instructor who thrives at the intersection of cutting-edge Generative AI technologies and impactful education. As a GenAI Instructor, you will shape the future of AI education by delivering industry-aligned content, mentoring learners, and fostering the mindset to build real-world AI systems using LLMs, AI agents, RAG pipelines, LangChain, LangGraph, AutoGen, CrewAI, Stable Diffusion, and more. Note: A strong background in Machine Learning (ML) and Deep Learning (DL) is non-negotiable. Familiarity with MLOps tools and workflows is considered a strong plus. Key Responsibilities 1. Curriculum Ownership & Development - Lead the design and iteration of a world-class curriculum around: > Applied Deep Learning > Applied Machine Learning > LLMs and Prompt Engineering > LangChain and LangGraph > AI Agents using CrewAI and AutoGen > RAG pipelines using LlamaIndex > Fine-tuning, RLHF, and MLOps > Stable Diffusion models > Multi-agent real-world AI projects - Continuously update content based on emerging industry trends. 2. Instructional Excellence - Deliver live, recorded, or blended sessions that simplify complex GenAI concepts. - Foster project-based learning environments with real-world AI use cases (e.g., hotel agent systems, ecommerce RAG agents). - Break down challenging tools like LangGraph, AutoGen, and Stable Diffusion for learners of all backgrounds. 3. Student Mentorship & Evaluation - Guide students in capstone projects covering agentic design, RAG, and GenAI deployments. - Provide timely and actionable feedback on assignments and presentations. - Mentor learners in building AI-first thinking and problem-solving skills. 4. Continuous Innovation - Integrate cutting-edge tools and APIs (Gemini, OpenRouter, HuggingFace, etc.) into the teaching stack. - Collaborate with internal teams to improve delivery, curriculum flow, and learning outcomes. 5. Industry Collaboration & Engagement - Engage in communities around open-source GenAI tooling and contribute thought leadership. - Stay active on platforms like GitHub, LinkedIn, Hugging Face, and LangChain community forums. Core Topics You'll Be Expected to Teach As a GenAI Instructor, you will be responsible for delivering comprehensive instruction and project-based learning across the following domains: 1. Applied Deep Learning - Neural networks, CNNs, RNNs using PyTorch - NLP and Computer Vision foundations for GenAI - Integrating DL models with LLM pipelines 2. Applied Machine Learning - Core supervised and unsupervised ML algorithms - Feature engineering, model evaluation, and pipeline design - ML system design for GenAI-backed applications 3. Programming & Data Foundations - Python and Python Libraries (e.g., NumPy, Pandas, Scikit-learn, Transformers) - Applied SQL for querying structured data in GenAI workflows - Applied Statistics for data-driven decision-making and model evaluation 4. Foundations of Generative AI - Introduction to Generative AI concepts and ecosystem - Ethical and responsible use of AI technologies - AI safety and alignment in the GenAI era 5. Large Language Models (LLMs) & Prompt Engineering - Understanding LLMs and transformer-based architectures - Crafting effective prompts for zero-shot and few-shot tasks - Hands-on projects using LangChain for LLM-based workflows 6. Building Agentic AI Applications - Developing applications using LangGraph, AutoGen, and CrewAI - Designing, orchestrating, and scaling AI agents and multi-agent systems - Implementing agent memory, tools, routing, and RAG workflows 7. Retrieval-Augmented Generation (RAG) Systems - RAG system architecture and design principles - Implementing vector search and indexing using LlamaIndex - Building production-ready GenAI applications with RAG pipelines 8. Fine-tuning and RLHF - Finetuning pre-trained LLMs for custom tasks - Training LLMs from scratch with small to medium datasets - Reinforcement Learning with Human Feedback (RLHF) fundamentals 9. MLOps for GenAI Applications - LLMOps: Building, monitoring, and deploying GenAI systems - AgentOps: Managing lifecycle of deployed AI agents - CI/CD pipelines, version control, evaluation, and scaling 10. Business & Strategic Applications of GenAI - Structuring AI solutions for real-world business use cases - Building GenAI strategies for domains like eCommerce, hospitality, and productivity - GenAI for leaders: frameworks, risks, and competitive positioning Qualifications - Minimum 2 years of experience in GenAI, AI/ML engineering, or Data Science Instructional roles. - Proven expertise in: - LLMs, LangChain, LangGraph, AutoGen, CrewAI - RAG systems (LlamaIndex, vector databases) - Stable Diffusion, Reinforcement Learning, RLHF - Python, PyTorch, APIs, Prompt Engineering - Strong foundation in Machine Learning and Deep Learning is mandatory. - Familiarity with MLOps workflows (e.g., CI/CD, monitoring, deployment) is a strong advantage. - Hands-on experience building or mentoring real-world GenAI applications. - Excellent verbal and written communication skills. - Demonstrated ability to break down complex technical systems into teachable components. Preferred Skills - Prior teaching/training experience in AI/ML/GenAI. - Active contributor to open-source GenAI tools or frameworks. - Experience with platform deployment, LLMOps, and agent orchestration. - Familiarity with product-led education or startup ecosystems.