Teaching Fellow - Agentic AI

Edversity is seeking a technically proficient and student-focused AI Engineer / Teaching Fellow to support its Agentic AI & Automation Engineering program. This hands-on role involves primary teaching responsibilities and curriculum delivery, with a strong emphasis on providing technical support to students. The ideal candidate will actively engage with student codebases, troubleshoot issues, and guide learners toward improved engineering practices, ensuring a practical understanding of AI beyond theory.

Key Responsibilities

- Live Class Support: Attend core live sessions alongside the Lead Instructor, monitor technical questions, and assist students with implementation challenges without interrupting the main lecture. Identify students needing additional technical help.

- Technical Support and Debugging: Assist students in troubleshooting issues related to Python, REST APIs, JSON, Git and GitHub, LLM APIs, Claude, n8n automation workflows, agentic AI systems, RAG, pgvector, MCP servers, FastAPI, PostgreSQL, Docker, deployment, OpenTelemetry, observability, AI security, and evaluation. Review student repositories to diagnose problems and provide clear, actionable guidance.

- Assignments, Labs, and Projects: Support learners through labs, assignments, and capstone projects by reviewing their work and offering practical technical feedback. Help students grasp concepts related to architecture, reliability, debugging, and production engineering.

- Technical Office Hours: Conduct at least one structured weekly technical support session to address debugging, assignment questions, and implementation issues that cannot be resolved during live classes. Identify recurring challenges and develop FAQs, examples, or technical resources to assist students.

- Collaboration with Lead Instructor: Partner closely with the Lead Instructor to identify learning gaps, recurring technical problems, and students who may be struggling. This collaboration allows the Lead Instructor to focus on delivering high-quality teaching while ensuring students receive consistent technical support.

Required Qualifications

- Minimum of 2 years’ experience in software, backend, automation, or AI engineering roles.
- Strong proficiency in Python and API development.
- Hands-on experience building applications powered by large language models (LLMs).
- Practical knowledge of agentic AI, automation, and retrieval-augmented generation (RAG).
- Familiarity with GitHub, FastAPI, databases, and Docker.
- Experience with tools such as n8n, MCP, and pgvector is highly advantageous.
- Excellent debugging and problem-solving skills.
- Ability to clearly explain complex technical concepts to learners with varying skill levels.
- Previous teaching or mentoring experience is preferred but not mandatory.

Preferred Qualifications and Attributes

- Technically strong and hands-on, with a focus on practical application rather than theory.
- Patient and supportive when working with learners.
- Proactive, responsible, and comfortable taking ownership of student issues.
- Skilled at reading and understanding unfamiliar codebases.
- Capable of simplifying complex concepts and identifying root causes effectively.

Success in This Role

Success will be measured by the timely resolution of student technical questions, quality of debugging and project support, reduction of unresolved issues, minimizing troubleshooting interruptions during lectures, and the overall improvement in student technical confidence and capability. Strong collaboration with the Lead Instructor is also a key indicator of success.

Candidate Assessment

Shortlisted candidates may be asked to complete a practical exercise involving a student project or a broken AI application. This will include identifying problems, debugging the implementation, recommending solutions, and explaining these solutions as they would to a student. Candidates should also be prepared to demonstrate AI or automation systems they have personally developed.