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.
Job Types: Full-time, Permanent

Pay: ₹450,000.00 - ₹850,000.00 per year

Benefits:

Flexible schedule Provident Fund Work from home
Work Location: Remote