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 PyTorchNLP and Computer Vision foundations for GenAIIntegrating DL models with LLM pipelines2. Applied Machine Learning
Core supervised and unsupervised ML algorithmsFeature engineering, model evaluation, and pipeline designML system design for GenAI-backed applications3. Programming & Data Foundations
Python and Python Libraries (e.g., NumPy, Pandas, Scikit-learn, Transformers)Applied SQL for querying structured data in GenAI workflowsApplied Statistics for data-driven decision-making and model evaluation4. Foundations of Generative AI
Introduction to Generative AI concepts and ecosystemEthical and responsible use of AI technologiesAI safety and alignment in the GenAI era5. Large Language Models (LLMs) & Prompt Engineering
Understanding LLMs and transformer-based architecturesCrafting effective prompts for zero-shot and few-shot tasksHands-on projects using LangChain for LLM-based workflows6. Building Agentic AI Applications
Developing applications using LangGraph, AutoGen, and CrewAIDesigning, orchestrating, and scaling AI agents and multi-agent systemsImplementing agent memory, tools, routing, and RAG workflows7. Retrieval-Augmented Generation (RAG) Systems
RAG system architecture and design principlesImplementing vector search and indexing using LlamaIndexBuilding production-ready GenAI applications with RAG pipelines8. Fine-tuning and RLHF
Finetuning pre-trained LLMs for custom tasksTraining LLMs from scratch with small to medium datasetsReinforcement Learning with Human Feedback (RLHF) fundamentals9. MLOps for GenAI Applications
LLMOps : Building, monitoring, and deploying GenAI systemsAgentOps : Managing lifecycle of deployed AI agentsCI / CD pipelines, version control, evaluation, and scaling10. Business & Strategic Applications of GenAI
Structuring AI solutions for real-world business use casesBuilding GenAI strategies for domains like eCommerce, hospitality, and productivityGenAI for leaders : frameworks, risks, and competitive positioningQualifications
Minimum 2 years of experience in GenAI, AI / ML engineering, or Data Science Instructional roles.Proven expertise in :LLMs, LangChain, LangGraph, AutoGen, CrewAIRAG systems (LlamaIndex, vector databases)Stable Diffusion, Reinforcement Learning, RLHFPython, PyTorch, APIs, Prompt EngineeringStrong 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.Skills Required
Machine Learning, Apis, Deep Learning, Pytorch, MLops, Python
