Curriculum Engineer - AI Engineering

1 - 3 years

7 - 11 Lacs

Posted:5 days ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

  • 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 3 5 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 1 5) from 70% of participants within two weeks of module completion, produce an analysis report, and implement prioritized changes within 4 6 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 Youll 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 fundamental

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