RETRIEVAL-AUGMENTED GENERATION (RAG) ENGINEERING INTERN

About the program About the Program EduRankAI is an Artificial Intelligence, Education Technology, Research, and Enterprise Technology organization developing intelligent digital platforms, enterprise software, and technology solutions for academia, industry, government, and global organizations. The RetrievalAugmented Generation RAG Engineering Internship provides practical exposure to building AI systems that combine large language models with advanced retrieval technologies to deliver accurate, contextaware, and reliable responses. Interns will contribute to the development of enterprise AI assistants, intelligent knowledge systems, document intelligence platforms, and conversational AI solutions by integrating retrieval pipelines with generative AI models. Working alongside artificial intelligence, machine learning, software engineering, data science, and research teams, interns will gain handson experience in designing, developing, evaluating, and optimizing RetrievalAugmented Generation architectures, vector databases, semantic retrieval systems, prompt orchestration pipelines, and knowledgeaware AI applications while strengthening their understanding of modern enterprise AI systems. Key Responsibilities Assist in developing RetrievalAugmented Generation RAG applications. Support document ingestion, preprocessing, and indexing pipelines. Build semantic retrieval and vector search workflows. Assist in integrating Large Language Models with retrieval systems. Develop prompt orchestration and context management pipelines. Support embedding generation and vector database management. Evaluate retrieval accuracy and response quality. Participate in enterprise AI assistant and document intelligence projects. Assist in optimizing retrieval latency and system performance. Prepare technical documentation, datasets, and engineering reports. Collaborate with AI, software engineering, data science, and research teams on live projects. Stay updated with advancements in generative AI, retrieval systems, and enterprise AI architectures. Perform additional RAG engineering assignments as required. Learning Outcomes Interns will gain practical exposure to: RetrievalAugmented Generation RAG Large Language Models LLMs Retrieval Pipelines Semantic Search Vector Databases Embedding Models Document Intelligence Prompt Engineering Prompt Orchestration Context Management Knowledge Retrieval AI Assistants Enterprise AI Systems AI Evaluation Retrieval Optimization Generative AI Engineering Technical Documentation Eligibility Students pursuing or having completed: B.E.B.Tech in Computer Science Engineering Artificial Intelligence Machine Learning Data Science Software Engineering Information Technology Information Science Electronics Communication Engineering M.E.M.Tech in relevant disciplines MCA M.Sc. in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, Information Science, or related disciplines Any equivalent programme Strong programming skills, analytical thinking, problemsolving abilities, and an interest in Artificial Intelligence, Large Language Models, Information Retrieval, and Enterprise AI Systems will be an added advantage but are not mandatory. Department Artificial Intelligence Data Science Qualification Type UG PG Qualification B.E. B.Tech M.E. M.Tech MCA M.Sc. Select Specialisation Computer Science Engineering Artificial Intelligence Machine Learning Data Science Software Engineering Information Technology Information Science Mathematics Statistics Electronics Communication Engineering Any Relevant Engineering or Science Discipline Perks Internship Certificate Letter of Recommendation (Performance Based) Mentorship from experienced AI and engineering professionals. Opportunity to contribute to live RAG, enterprise AI, and intelligent assistant projects. Exposure to Large Language Models, vector databases, semantic retrieval, document intelligence, and enterprise AI architectures. Professional development sessions on Generative AI, software engineering, retrieval systems, and AI research methodologies. Outstanding performers may be considered for extended internships, leadership opportunities, or future full-time positions based on organizational requirements. 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 Internship Type: Full-Time Mode of Internship: Online Duration: 3 Months Working Days: 6 Days per Week Total Engagement: Approximately 40 Hours Per Week (?480 Hours over 12 weeks) Project Work / Departmental Responsibilities: Approximately 5 Hours Per Day Holistic Well-being & Personal Development: Approximately 1 Hour 40 Minutes (1.67 Hours) Per Day, including physical fitness, mindfulness, reading, reflective learning, leadership development, community engagement, and other approved personal development activities. Weekly mentor reviews and performance evaluations will be conducted. Interns are expected to maintain professionalism, confidentiality, ethical conduct, and adherence to organizational policies. This is an unpaid internship. Internship Certificate will be awarded upon successful completion of internship requirements. Internship does not constitute an offer of employment. Number of openings 10