Gen AI Curriculum and Systems Engineer

This role sits where GenAI engineering meets learning content. You'll design and run the agentic systems that generate and quality-check what learners study, and you'll own the outcomes those systems produce, not just the code behind them. You will play a key role in designing, developing, and enhancing learning programs. This role is ideal for individuals who enjoy creating learning experiences and structuring educational content.

You'll build the AI systems that generate and quality-check our learning content (lesson scripts, coding exercises, graded projects, MCQs) and own that content through to what reaches learners. The agents are how the work gets done; what you're accountable for is content that ships quickly, holds up under assessment, and stays current.

Responsibilities:

Design, develop, and maintain curriculum, learning modules, and assessments for Generative AI programs through Agentic Workflows.

Create engaging learning content, including lesson plans, presentations, assignments, projects, coding exercises, and quizzes.

Own the quality bar: design the eval sets, rubrics, and guardrails that decide what's good enough to ship, and author enough content yourself to keep that judgment sharp.

Keep content accurate and current: catch outdated Gen AI concepts, deprecated libraries, wrong syntax, or broken code examples within a week of discovery, and fix them within SLA.

Contribute to curriculum R& D: experiment with new tools and frameworks, and proactively flag what's worth adding or retiring, with at least one R& D-driven update or recommendation each quarter.

Build the foundations that keep the pipeline running and improving on its own: memory, knowledge bases, integrations, observability, and clearing the issues that stall generation or loading.

Requirements:

1-2 years building software or AI/automation systems, with at least one deployed, evaluated agentic workflow or LLM-integrated app you can show us (GitHub or live demo, not a tutorial follow-along).

SME-level working knowledge of GenAI (prompt design, RAG, vector databases, hosted LLM APIs), plus the content ability to judge, produce, and correct technically accurate learning material.

MERN + Python, enough to build the surfaces your agents plug into and write your own logic and integrations.

Hands-on with an agent/automation framework (n8n, LangGraph, LangChain, CrewAI, or equivalent), including multi-step, tool-calling workflows.

An evaluation instinct: you build the rubric before you trust the output, and treat an unmeasured agent as unfinished.

A feel for where things break, from hallucination and prompt injection to cost at scale and latency, and how to guard against it.

You use AI tools (Claude, Cursor, ChatGPT, Copilot) as a real part of how you build, every day.

You write clearly: precise, structured, easy to follow.

Nice-to-have:

EdTech, assessment, or structured-content experience.

Observability and eval tooling (LangSmith, RAGAS, or similar), and CI/CD for content pipelines.

A public trail of how you learn: a thoughtful README, a walkthrough, a blog post, an open-source contribution.