Role overview This role sits where GenAI engineering meets learning content. You'll help design and build the agentic systems that generate and quality-check what learners study, working closely with the team as you grow into owning parts of the pipeline yourself. It suits someone early in their journey who's already as excited about shipping an agent as they are about judging whether the content it created is actually positive, and who cares whether learners succeed, not just whether something shipped.

What you'll do (and grow into) You'll help build the AI systems that generate and quality-check our learning content (lesson scripts, coding exercises, graded projects, MCQs), and take on more ownership of that content as it reaches learners the more you show you can. The agents are how the work gets done; what we'll grow you toward is content that ships quickly, holds up under assessment, and stays current. Build and ship parts of the agentic workflows (n8n, LangGraph, LLM APIs) that generate learning content, and help hold them to our bar for instructional clarity and technical accuracy, contributing to a 4/5-or-better instructor rating.

Help build the quality layer: the eval sets, rubrics, and guardrails that decide what's good enough to ship, and author content yourself to keep sharpening your own judgment.

Keep content accurate and current: spot outdated Gen AI concepts, deprecated libraries, wrong syntax, or broken code examples, and fix them promptly, with support on the trickier calls.

Get involved in curriculum R&D;: try out new tools and frameworks as they land,



and share what's worth adding or retiring.

Learn the foundations that keep the pipeline running and improving: memory, knowledge bases, integrations, and observability, and help clear the issues that stall generation or loading.

What your work feeds into: Content: velocity (more shipped per cycle), efficiency (lower cost and effort per unit), effectiveness (learners who complete it pass what they're assessed on), and relevancy (it stays current).

Business: skill-assessment success percentage, a minimal delta between content and assessment, and more learners landing in the ideal engagement bands. Must-have 0–1 year of building software or AI projects — internships, personal, course, or hackathon work all count — with at least one LLM-integrated app or agent you can show us (GitHub or live demo, not a tutorial follow-along).

Genuine hands-on familiarity with GenAI (prompt design, basic RAG, vector databases, calling hosted LLM APIs), and enough content sense to tell technically accurate material from sloppy.

Comfortable in Python, plus some full-stack (MERN or similar),



enough to build simple surfaces your agents plug into and write your own logic and integrations.

Have used an agent/automation framework (n8n, LangGraph, LangChain, CrewAI, or equivalent) to build at least a multi-step, tool-calling workflow.

An evaluation instinct in the making: you check your output against something before you trust it, rather than assuming it's right.

Curiosity about where things break — wrong answers, hallucination, cost — and the willingness to dig in and figure out why.

You already use AI tools (Claude, Cursor, ChatGPT, Copilot) as a normal part of how you build.

A fast learner: you can pick up a new tool or concept from docs, AI, and a bit of guidance without needing step-by-step instruction.

You write clearly: precise, structured, easy to follow. Nice-to-have Any EdTech, tutoring, teaching, or content-creation experience, even informal.

Exposure to observability or eval tooling (LangSmith, RAGAS, or similar), or to CI/CD.

A public trail of how you learn: a thoughtful README, a walkthrough, a blog post, an open-source contribution, or a project demo. Why this role GenAI content is being figured out as it's built, and this is a rare first or second role where you help shape how it's done rather than run someone else's playbook. You'll build real systems, see their impact on real learners, and grow fast, with room to experiment, ship, and improve.

If you've built something you're proud of and you're hungry to build a lot more, we'd love to see it.

Location: Hyderabad (In-office)

Workweek: 5 days

📌 Associate GenAI Engineer (Hyderabad)
🏢 NxtWave
📍 Hyderabad