Content Lead - AI Engineering

Role Summary

As a Content Lead for AI Engineering, you’ll own everything related to building applications with AI. That includes designing the curriculum, writing reading materials, building assessments, creating lab exercises, and developing projects around LLM application development, agentic workflows, RAG systems, and multi-modal AI.

This is a hands-on creation role. You’ll spend most of your time building, testing, and improving content. You’ll also be shipping AI applications and prototypes yourself regularly, because this space moves too fast for someone who’s just reading about it. If you haven’t built a RAG pipeline or debugged an agent loop yourself, you can’t teach someone else to do it well.

If you’re someone who’s been building with LLMs since before it was cool, understands why agents fail more often than they work, and can turn a complex AI architecture into something a student can build step by step, this is your role.

What You’ll Do

• Design semester-wise curriculum and decide how topics are structured, sequenced, and paced across the academic calendar.
• Continuously evolve the content strategy based on what’s working, what’s not, and where the AI engineering landscape is heading. This space changes fast and the content needs to keep up.
• Write clear, in-depth reading materials that students can actually learn from independently. Not hype-driven overviews, but grounded explanations of how things actually work under the hood.
• Design problems that test whether students understand the architecture and trade-offs behind AI applications, not just whether they can chain a few API calls together.
• Build question banks following ByteXL’s quality standards for difficulty distribution, answer balance, and auto-gradability.
• Create hands-on lab exercises that take students from their first API call to building production-ready AI applications.
• Design capstone projects that push students to build complete AI applications integrating LLMs, vector stores, retrieval pipelines, and deployment.
• Build AI applications and prototypes yourself regularly to validate that what you’re asking students to do is actually doable, meaningful, and teaches the right lessons.
• Stay current with how the AI engineering ecosystem is evolving.
• Try new ways of teaching and building content. If something could work better, prototype it and make the case.

What We’re Looking For

Must Have

• B.Tech, M.Tech, MCA, or equivalent in Computer Science or a related field.
• 35 years of experience in AI/ML engineering, software development, or content development with a strong focus on applied AI.
• Expert-level Python proficiency.
• Hands-on experience building LLM-powered applications, not just experimenting in notebooks.
• Deep understanding of prompt engineering, RAG architectures, and vector databases.
• Experience building AI agents and agentic workflows. Understands why they break and how to make them reliable.
• Working knowledge of frameworks like LangChain, LlamaIndex, or similar.
• Experience integrating AI APIs from providers like OpenAI, Anthropic, and others.
• Understanding of multi-modal AI concepts spanning text, image, and audio.
• Awareness of responsible AI principles and AI safety considerations.
• Exposure to MLOps practices for AI application deployment and monitoring.
• Can explain how a retrieval pipeline works or why an agent keeps hallucinating clearly enough that someone building their first AI app actually gets it.
• Works independently without needing constant direction.

Nice to Have

• Working knowledge of TypeScript for frontend integration with AI backends.
• Experience with fine-tuning and model adaptation techniques.
• Has built and open-sourced AI tools or applications.
• Background in technical training, academic teaching, or developer education.
• Active presence in the AI engineering community through blogs, talks, or open-source work.