AI Faculty - Part Time (Weekends only)

About the Role We are looking for experienced AI practitioners to join our faculty team on a part-time basis for weekend batches for our online live programs (4 to 5 hours with 2 breaks) This is not a conventional academic teaching role. We are specifically looking for professionals who have hands-on experience implementing AI in real-world business or technology environments and can translate that experience into practical, engaging learning for students. The ideal candidate has built, deployed, integrated, or managed AI solutions and can explain complex concepts clearly through practical examples, demonstrations, and projects. Depending on your expertise, you may teach modules across one or both of our professional AI programs. Key Responsibilities -Deliver live, instructor-led sessions during weekends (live online sessions only) -Teach AI concepts through practical examples, demonstrations, case studies, and hands-on exercises. -Explain how AI technologies are implemented in real-world environments. -Guide students through practical projects and implementation-oriented assignments. -Conduct demonstrations and technical workshops. -Answer student questions and provide technical guidance. -Review assignments and projects where required. -Provide constructive feedback on student implementation. -Keep course content aligned with current AI technologies, tools, frameworks, and industry practices. -Collaborate with the academic team to improve course content and practical exercises. -Follow established teaching, assessment, and academic standards. Areas of Expertise We are looking for faculty with expertise in three or more of the following areas. AI / Professional AI Artificial Intelligence and Machine Learning Python for AI Machine Learning Deep Learning Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) AI Agents Natural Language Processing Computer Vision AI application development AI APIs and model integration AI deployment MLOps AI evaluation and optimization Agentic AI Generative AI and LLMs Agentic AI AI agent architectures Tool/function calling Multi-agent systems RAG and agentic RAG AI workflows and orchestration LangGraph CrewAI AutoGen OpenAI Agents SDK AI application development Agent deployment and monitoring Agent evaluation Production AI systems AI security and reliability You do not need to be an expert in all of these areas. We are interested in specialists who can teach the areas where they have genuine expertise. Critical Requirement — Real-World AI Implementation This is the most important requirement. We are looking for AI practitioners, not purely academic instructors. You should have experience actually implementing AI systems, products, solutions, or workflows. Relevant experience could include: -Building and deploying AI/ML solutions -Developing LLM applications -Building AI agents -Implementing RAG systems -Integrating AI APIs into applications -Building AI automation systems -Deploying models into production -Developing AI-powered products -Implementing enterprise AI solutions -Building AI platforms -Implementing MLOps -Leading AI implementation projects -Using AI to solve real business or operational problems During the interview, you should be able to explain what you implemented, why you implemented it, how it was built, what technologies you used, and what challenges you encountered. What We Are Looking For: -Strong practical AI experience -Demonstrable AI implementation experience -Strong understanding of modern AI technologies -Excellent communication and presentation skills -Ability to explain complex concepts clearly -Ability to teach students with different levels of technical experience -Practical, problem-solving mindset -Experience with current AI tools and frameworks -Ability to connect theory with real-world implementation -Willingness to stay current with rapidly evolving AI technologies -Professional and reliable approach to teaching -Teaching Approach We prefer practitioner-led teaching rather than slide-based lectures. A strong session should ideally follow: Concept → Demonstration → Implementation → Real-World Example → Student Exercise Students should understand not only what a technology is, but also how to actually use it. Schedule This is a part-time weekend engagement. Faculty will be expected to conduct scheduled live online sessions on weekends. The exact schedule and number of sessions will depend on the assigned modules and cohort requirements. There may also be opportunities to teach across both programs if your expertise matches multiple areas. To Apply Please provide: -Your updated CV or LinkedIn profile -A brief summary of your AI implementation experience -Examples of AI projects, products, systems, or implementations you have worked on -The AI topics/modules you are comfortable teaching -Your weekend availability -Links to relevant work, GitHub, portfolio, publications, or demonstrations where available Please focus your application on actual AI implementation experience. Applications based primarily on academic knowledge without practical AI implementation experience are unlikely to be suitable for this role.