KNOWLEDGE GRAPH 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 Knowledge Graph Engineering Internship provides practical exposure to knowledge representation, semantic technologies, ontology engineering, graph databases, linked data, entity resolution, relationship modeling, and intelligent knowledge systems. Interns will contribute to the development of structured knowledge graphs that enable artificial intelligence systems to understand relationships between entities, improve semantic search, power recommendation engines, enhance RetrievalAugmented Generation RAG, and support intelligent decisionmaking across enterprise applications. Working alongside artificial intelligence, machine learning, software engineering, data science, and research teams, interns will gain handson experience in designing, constructing, querying, integrating, and maintaining knowledge graphs while strengthening their understanding of semantic AI, graph analytics, information retrieval, and knowledgedriven intelligent systems. Key Responsibilities Assist in designing and developing knowledge graph architectures. Support ontology design, schema development, and semantic data modeling. Build and maintain graph databases and knowledge repositories. Perform entity extraction, entity linking, and entity resolution. Develop relationship extraction and knowledge integration pipelines. Support graph data ingestion, transformation, and enrichment processes. Assist in implementing semantic search and knowledge discovery solutions. Participate in graph analytics and knowledge reasoning projects. Evaluate knowledge graph quality, completeness, and consistency. 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 semantic technologies, graph databases, and knowledge engineering. Perform additional knowledge engineering assignments as required. Learning Outcomes Interns will gain practical exposure to: Knowledge Graph Engineering Knowledge Representation Ontology Engineering Semantic Technologies Graph Databases RDF OWL SPARQL Linked Data Entity Resolution Entity Linking Knowledge Extraction Graph Analytics Semantic Search Knowledge Reasoning Graph Data Modeling Artificial Intelligence Knowledge Systems Technical Documentation Eligibility Students pursuing or having completed: B.E.B.Tech in Computer Science Engineering Artificial Intelligence Data Science Machine Learning Information Technology Software Engineering 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, Computational Linguistics, or related disciplines Any equivalent programme Strong programming skills, analytical thinking, logical reasoning, data modeling abilities, and an interest in Artificial Intelligence, semantic technologies, and knowledge engineering 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 Computational Linguistics 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 knowledge graph, semantic AI, and enterprise intelligence projects. Exposure to graph databases, semantic technologies, ontology engineering, knowledge representation, and AI-powered knowledge systems. Professional development sessions on Artificial Intelligence, semantic web technologies, graph engineering, software engineering, and 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