Content Lead - AI Engineering

Role Summary

As a Content Lead for AI Engineering, you'll own the entire learning journey that takes students from calling their first LLM API to building, evaluating, and deploying agentic AI systems in production. That includes designing the curriculum, writing reading materials, building assessments, creating lab exercises, and developing projects that teach students how AI products are actually built, not just how models work.

This is a hands-on creation role. You'll spend most of your time building, testing, and improving content. You'll also be building RAG pipelines, agents, and evaluation setups yourself regularly, because this field moves fast and content written by someone who hasn't shipped with the current tools goes stale quickly. If you can't build it, you can't teach it.

If you're someone who can design a RAG system, figure out why it's returning bad answers, and then explain both clearly enough that a student building their first AI app gets it right, 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, from LLM fundamentals to agentic systems and production deployment.
• Keep the content aligned with what the industry hires for. Map topics to real roles such as AI engineer, applied AI developer, and agent developer.
• Continuously evolve the content strategy based on what's working, what's not, and where the AI tooling ecosystem is heading. In this space, that can change within a few months.
• Write clear, in-depth reading materials that students can learn from independently. Not API documentation rewrites or hype-driven overviews, but well-structured explanations with working code, real design trade-offs, and failure cases.
• Design problems that test whether students understand how AI systems behave and break, such as retrieval quality, prompt design, tool use, and cost and latency, not just whether they can paste in a framework example.
• Build question banks following ByteXL's quality standards for difficulty distribution, answer balance, and auto-gradability.
• Create hands-on lab exercises that progressively build complexity, from prompting and structured outputs to retrieval, tool calling, multi-step agents, and evaluation.
• Design capstone projects and mini-apps that push students to integrate models, data, backend, evaluation, and deployment into a single working AI application.
• Build full sample projects yourself regularly to validate that what you're asking students to do is doable, meaningful, and teaches the right lessons.
• Teach evaluation, guardrails, safety, and responsible AI as a regular part of building, not an afterthought.
• Stay current with new models, frameworks, protocols, and techniques, and decide what is worth teaching and what is noise.
• 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.
• 3-5 years of experience in software engineering, applied AI, technical training, or content development, with hands-on work building LLM-based applications.
• Strong Python proficiency, with working knowledge of a backend framework such as FastAPI.
• Solid understanding of how LLMs work at a practical level, including tokenization, embeddings, context windows, sampling, and fine-tuning versus prompting versus retrieval.
• Hands-on experience building RAG systems, including chunking strategies, vector databases, and retrieval and reranking techniques.
• Experience building agents with tool calling, multi-step workflows, memory, and orchestration frameworks such as LangChain, LangGraph, or similar, and knowing when not to use a framework.
• Familiarity with protocols and patterns such as MCP and structured outputs.
• Experience evaluating AI systems, including building test sets, using LLM-as-judge approaches, and measuring quality, cost, and latency.
• Working knowledge of deploying and monitoring AI applications using Docker, cloud platforms, and observability tooling.
• Has shipped real AI projects or products, not just notebooks or tutorials.
• Can explain how a RAG pipeline works end to end, or why an agent loops and fails, clearly enough that someone building their first AI app understands it.
• Works independently without needing constant direction.

Nice to Have

• Experience with fine-tuning techniques such as LoRA, or with running open-weight models locally or on your own infrastructure.
• Experience with multimodal or voice-based applications.
• Experience with LLMOps and tracing tools such as LangSmith, MLflow, or Weights & Biases.
• Knowledge of AI security topics such as prompt injection and data leakage.
• Prior open-source contributions, published work, or a track record in AI hackathons.
• Background in technical training, bootcamp instruction, or developer education.
• Experience designing auto-graded coding assessments.