Curriculum Engineer

Job Overview

We are hiring a Curriculum Engineer to design and enhance our AI/ML program covering Python, foundational mathematics, machine learning, deep learning, large language models (LLMs), retrieval-augmented generation (RAG) systems, and AI agents.

• Open to freshers with strong project portfolios and demonstrable hands-on AI/ML work.
• Candidates with practical experience in AI tools—especially AI agents, RAG systems, and LLM applications—are strongly preferred.

What You'll Do
Curriculum Development & Quality Management

• Design, develop, and maintain curriculum across Python, math for AI, ML, DL, LLMs, RAG systems, and AI agents.
• Create end-to-end learning materials: pre-reads, lecture notes, slides, coding assignments, case studies, and hands-on projects.
• Build assessments, quizzes, and rubrics to measure and improve student outcomes.
• Work with faculty, industry mentors, instructors, and operations to ensure material quality and coherence.
• Implement feedback loops to iterate on content and learning experiences.
• Keep content current with the latest AI/ML research and industry best practices.

Collaboration & Professional Growth

• Collaborate closely with professors from premier institutes and industry experts.
• Stay updated with AI/ML developments through continuous engagement with practitioners.
• Provide technical guidance and mentorship to students where required.

What You'll Need

Strong foundation across these technical areas. Bullet points highlight core expectations for quick scanning.

Technical Skills

• Python: Proficiency in Python, data manipulation with NumPy/Pandas, visualization, Git/GitHub, and common tools (Jupyter, CLI).
• Mathematics for AI: Linear algebra, calculus, statistics/probability, and optimization fundamentals.
• Machine Learning: Supervised/unsupervised methods, model evaluation, scikit-learn workflows, hyperparameter tuning, and basic MLOps concepts.
• Deep Learning: Neural networks, CNNs, sequence models, Transformers, fine-tuning (e.g., BERT), generative models, and PyTorch or TensorFlow/Keras proficiency.
• LLMs & Modern AI: LLM architectures, tokenization, embeddings, attention, working with LLM APIs (OpenAI, Anthropic, Hugging Face), prompt engineering, and fine-tuning techniques (LoRA/PEFT).
• RAG & AI Agents (preferred): Vector databases, embedding pipelines, RAG systems, agent architectures (e.g., LangGraph), tool-calling, memory strategies, and deployment (Docker/cloud).
• Data & Ethics: Data collection/cleaning, ethical AI practices, bias awareness, and familiarity with AutoML/automated workflows.

Projects & Practical Experience

• Strong project portfolio (GitHub, Kaggle, personal projects) demonstrating hands-on AI/ML work.
• Experience building or working with AI agents and RAG systems is highly valued.
• Evidence of end-to-end pipelines or deployed ML/DL applications.
• Technical depth shown via code, documentation, or technical writing (blogs, tutorials).

Educational Background & Experience

No specific degree or years of experience required. We prioritise demonstrable skills and real work artifacts. Recent graduates, self-taught candidates, bootcamp alumni, and career switchers are welcome.

Skills & Attributes

• Clear communication and the ability to explain complex concepts simply.
• Strong collaboration and stakeholder management skills.
• Detail-oriented with a commitment to high-quality content.
• Passion for education and mentoring.
• Self-motivated learner who stays current with AI research and tools.
• Prior ed-tech or content development experience is a plus.

Why Join Masai?

• Collaborate with professors from IITs, IIMs, and industry leaders to build world-class AI/ML curriculum.
• Expand your professional network through top academic and industry collaborations.
• Stay at the forefront of AI/ML through exposure to cutting-edge research and technologies.
• Impact thousands of students by shaping the future of AI education.
• Fast-paced startup environment with clear growth opportunities.
• Competitive compensation and benefits.
• Hands-on innovation: work on educational technology and practical AI systems (models, RAG, agents) alongside curriculum development.

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