Data Science and Gen AI Instructor

AlmaBetter; India; On-site; INR 800,000 - 1,200,000; Full time

Job summary

A leading online education platform is seeking a Data Science and Gen AI Instructor to deliver industry-aligned content and mentor learners in AI education. This role requires a strong foundation in Machine Learning, Deep Learning, and Generative AI with at least 2 years of instructional experience. The position offers a full-time contract and an opportunity to shape the future of AI education in India.

Qualifications

• Minimum 2 years of experience in GenAI, AI/ML engineering, or Data Science Instructional roles.
• Excellent verbal and written communication skills.
• Demonstrated ability to break down complex technical systems into teachable components.

Responsibilities

• Lead the design and iteration of a world-class curriculum around LLMs and Prompt Engineering.
• Deliver live, recorded, or blended sessions that simplify complex GenAI concepts.
• Mentor learners in building AI-first thinking and problem-solving skills.

Skills

Python

Machine Learning

Deep Learning

GenAI

Prompt Engineering

Education

Experience in AI/ML engineering or Data Science Instructional roles

Tools

PyTorch

APIs

Job description

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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

• Lead the design and iteration of a world-class curriculum around:
  • LLMs and Prompt Engineering
  • LangChain and LangGraph
  • AI Agents using CrewAI and AutoGen
  • Fine-tuning, RLHF, and MLOps
  • Multi-agent real-world AI projects
• Continuously update content based on emerging industry trends.

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, e‑commerce RAG agents).
• Break down challenging tools like LangGraph, AutoGen, and Stable Diffusion for learners of all backgrounds.
• 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.
• 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.

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

• NLP and Computer Vision foundations for GenAI
• Integrating DL models with LLM pipelines
• Core supervised and unsupervised ML algorithms
• Feature engineering, model evaluation, and pipeline design
• ML system design for GenAI‑backed applications

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

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
• 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

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

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

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

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

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.
• 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.

Seniority level

Entry level

Employment type

Full‑time

Job function

Engineering and Information Technology

Industries

Higher Education

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