Data Science And Gen AI Instructor

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 Youll 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.