AQUINTUTOR AI TUTOR INTERN – APPLIED AI & LLM ENGINEERING

About the program EduRankAI is inviting applications for the position of AquinTutor AI Tutor Intern to join our AI Tutor team. This fulltime remote internship is designed for individuals who are passionate about Artificial Intelligence, Large Language Models LLMs, Machine Learning, prompt engineering, model evaluation, educational AI, and applied AI research. As an AI Tutor Intern, you will contribute to developing and improving AI powered tutoring systems by designing evaluation frameworks, conducting prompt engineering experiments, analysing model performance, and building reusable evaluation infrastructure. You will work closely with Senior and Staff Machine Learning Engineers on real world AI projects that directly impact learners using EduRankAI's AI powered education platform. Your responsibilities will include building reusable evaluation harnesses for Large Language Models, designing and executing prompt engineering and retrieval experiments, analysing quantitative and qualitative model performance, evaluating AI system outputs using structured methodologies, documenting experimental findings and research outcomes, preparing methods and findings reports, supporting AI model testing and benchmarking, collaborating with engineering and research teams, presenting internship outcomes during the end of internship review, and contributing to continuous improvements in AI tutor quality, reliability, and educational effectiveness. Applicants should possess strong analytical thinking, programming skills, problem solving ability, structured communication, curiosity for AI research, attention to detail, collaboration skills, and an evaluation first mindset. Familiarity with Python, PyTorch, Large Language Model APIs, Retrieval Augmented Generation RAG, prompt engineering, machine learning fundamentals, data analysis, Git, Jupyter Notebook, experimentation methodologies, and AI evaluation techniques will be considered an advantage. Prior experience developing projects using LLM APIs or conducting machine learning experiments is desirable but not mandatory. Comprehensive mentorship, structured guidance, and continuous learning support will be provided throughout the internship. The internship is Remote for a duration of three months and requires a commitment of eight hours per day, six days per week. Successful interns will gain practical experience in applied AI, prompt engineering, LLM evaluation, AI experimentation, machine learning workflows, educational AI systems, technical documentation, AI research methodologies, and cross functional collaboration while contributing directly to the advancement of EduRankAI's AI Tutor platform. This is an unpaid internship designed to provide meaningful industry exposure, structured mentorship, real world AI engineering experience, portfolio development, and outstanding performers may be considered for advanced AI projects, internship extensions, preplacement interviews, or fulltime opportunities based on performance and organizational requirements. Perks Certificate of Completion Letter of Recommendation for exceptional performers Mentorship from Senior and Staff Machine Learning Engineers Hands-on experience with live AI Tutor and LLM engineering projects Opportunity to contribute to real-world AI evaluation and prompt engineering initiatives Professional portfolio development Exposure to applied AI, machine learning, LLM evaluation, prompt engineering, AI research, and educational technology Performance-based Pre-Placement Interview opportunity Cross-functional collaboration with AI, engineering, product, curriculum, and research teams Who can apply? Only those candidates can apply who: are from Any and specialisation from Any are available for duration of 3 Months have relevant skills and interests Terms of Engagement This is a full-time Remote internship requiring a commitment of 8 hours per day, 6 days per week for a duration of three months. Interns will contribute to live AI Tutor engineering initiatives under structured mentorship while maintaining professionalism, documentation quality, technical excellence, research integrity, and adherence to project timelines. Participants will build reusable evaluation frameworks, conduct prompt and retrieval experiments, analyse AI model performance, document research findings, collaborate with Senior and Staff ML Engineers, and present their work during the end-of-internship review. Successful completion of the internship will be evaluated based on technical competency, analytical ability, research quality, communication, collaboration, ownership, consistency, professionalism, and overall contribution throughout the internship. Number of openings 10