Curriculum Engineer

Job Description

Job Overview

We are looking for a Curriculum Engineer to develop and enhance our AI Engineering program focused on building production-ready AI applications with LLMs, RAG systems, and agentic workflows.

This role is also open to freshers with strong project portfolios demonstrating hands-on knowledge in AI Engineering. Candidates with practical experience in AI tools, especially AI agents, agentic systems, and LLM-based applications, are strongly preferred.

What You'll Do

Curriculum Development & Quality Management

- Design, develop, and update curriculum content across Python programming, backend engineering, LLM foundations, RAG systems, agentic architectures, and AI application deployment. Deliver at least 2 major modules or 4 minor modules per quarter, each with 35 clearly defined learning objectives and acceptance criteria.
Create end-to-end learning materials for each module including: 1 pre-read (Develop formative and summative assessments, quizzes, and evaluation rubrics to measure student learning outcomes. Define mastery thresholds, target an average cohort pass rate of at least 70% for core assessments, and track item-level statistics to identify weak concepts.
- Collaborate with professors from IITs/IIMs, industry mentors, instructors, and operations teams to ensure quality of all session materials. Run a documented peer-review cycle for each module with a minimum of two reviewers and close critical review items before release.
- Implement feedback mechanisms to continuously improve content quality and student outcomes: collect quantitative feedback (Likert 15) from 70% of participants within two weeks of module completion, produce an analysis report, and implement prioritized changes within 46 weeks. Aim to improve average quality ratings by 0.3 points quarter-over-quarter.
- Ensure curriculum stays current with rapidly evolving AI engineering tools and best practices by performing a monthly landscape scan and executing targeted content updates at least once per quarter; add at least one new tool or hands-on tutorial to the curriculum each quarter.

Collaboration & Professional Growth

- Work closely with faculty from premium institutes and industry experts to build your professional network. Maintain bi-weekly stakeholder syncs and coordinate at least one guest lecture or industry session per semester.
- Stay updated with the latest developments in AI engineering, LLMs, and agentic systems through continuous collaboration with leading practitioners. Participate in or host at least one knowledge-sharing session per month and provide a short summary of key takeaways to the team.
- Provide technical guidance and support to students via weekly office hours (minimum 2 hours/week), respond to student queries within 48 business hours, and mentor up to 10 students per cohort with documented progress checkpoints.

What You'll Need

Technical Knowledge

Programming & Software Engineering Foundations

- Python Programming: Strong proficiency in Python fundamentals, OOP, libraries, virtual environments
- Data Structures & Algorithms: Basic DSA concepts and problem-solving
- Web Fundamentals: APIs, HTTP/HTTPS, client-server model, REST principles
- Backend Engineering: CRUD operations, FastAPI, data validation with Pydantic, authentication & authorization
- Databases: SQL, database modeling, migrations
- Developer Workflow: Git, GitHub, version control best practices
- Frontend Basics: HTML, CSS, DOM, JavaScript fundamentals

LLM Foundations & Multimodality

- LLM APIs: Working with OpenAI APIs, Hugging Face, Ollama, and local LLMs
- Prompt Engineering: Crafting effective prompts, understanding tokens, context windows, temperature/top-p control
- LLM Internals: Understanding how LLMs work under the hood
- Vector Embeddings: Vector databases, semantic search, similarity metrics
- Multimodal AI: Working with text, images, audio, and video in AI applications

RAG & Agentic Systems (Strongly Preferred)

- RAG Pipelines: Building Retrieval-Augmented Generation systems with vector databases, chunking strategies
- LangChain: Hands-on experience building agent architectures and workflows
- Agentic Workflows: MCP (Model Context Protocol), multi-agent systems, agent orchestration
- RAG Evaluation: Guardrails, quality metrics, evaluation frameworks
- AI Coding Tools: Experience with Claude Code, Cursor AI, or similar agentic development tools

Advanced Topics

- LangGraph: Building robust multi-agent workflows and state machines
- LLM Finetuning: Basic understanding of finetuning approaches and use cases
- Deployment & Evaluation: LLM application deployment, performance evaluation, monitoring

Projects & Practical Experience

- Strong project portfolio demonstrating hands-on work in AI engineering (GitHub repositories with RAG systems, agents, LLM applications)
- Practical experience building or working with AI agents and agentic systems is highly preferred
- Evidence of building end-to-end AI applications from prototype to deployment
- Technical depth through code, documentation, or technical writing (blog posts, tutorials, etc.)

Educational Background & Experience

We don't require a specific degree or years of experience. What matters to us is your strong foundational knowledge across the concepts mentioned above and your ability to demonstrate it through real work.

Whether you're a recent graduate, a self-taught developer, a bootcamp alumnus, or a career switcher - if you have the skills and can show us your projects, we want to hear from you. Your proof of work is your credential.

Skills & Attributes

- Excellent communication skills with ability to explain complex AI engineering concepts clearly
- Strong collaboration and stakeholder management abilities
- Detail-oriented with commitment to quality
- Passion for education and mentoring aspiring AI engineers
- Self-motivated continuous learner who keeps pace with fast-evolving AI tooling
- Prior experience in ed-tech or content development is a plus

Why Join Masai

- Work with professors from IITs, IIMs, and industry leaders to build world-class AI engineering curriculum
- Expand your professional network through collaboration with top academic and industry experts
- Stay at the forefront of AI engineering developments through continuous exposure to cutting-edge tools and frameworks
- Shape the future of AI engineering education and impact thousands of students careers
- Fast-paced startup environment with growth opportunities
- Competitive compensation and benefits
- Build while you teach: Design and develop production-grade AI engineering tools and platforms for ed-tech, gaining hands-on experience with LLMs, agentic systems, and scalable AI architectures in a real startup environment

Skills: llm,learning,building,foundations,agents,one,project,artificial intelligence,collaboration,databases,concepts,rag,fundamentals