AI INFRASTRUCTURE 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 AI Infrastructure Engineering Internship provides practical exposure to designing, developing, deploying, and maintaining scalable infrastructure for artificial intelligence systems. Interns will contribute to building highperformance AI platforms that support model training, inference, distributed computing, data pipelines, GPU resource management, containerized applications, cloudnative deployments, and enterprise AI services. Working alongside artificial intelligence, machine learning, software engineering, cloud engineering, DevOps, and research teams, interns will gain handson experience in developing robust AI infrastructure, deployment pipelines, distributed computing environments, orchestration platforms, and scalable backend systems while strengthening their understanding of productiongrade AI engineering. Key Responsibilities Assist in developing scalable AI infrastructure and backend services. Support deployment of machine learning and AI models in production environments. Develop containerized AI applications and deployment workflows. Assist in managing GPU computing resources and AI workloads. Support distributed computing and parallel processing environments. Participate in AI platform automation and infrastructure optimization projects. Assist in monitoring system performance, availability, and reliability. Support cloudnative deployment and infrastructure orchestration. Contribute to infrastructure documentation, technical reports, and operational procedures. Collaborate with AI, software engineering, DevOps, cloud engineering, and research teams on live projects. Stay updated with advancements in AI infrastructure, cloud computing, and distributed systems. Perform additional AI infrastructure engineering assignments as required. Learning Outcomes Interns will gain practical exposure to: AI Infrastructure AI Platform Engineering Distributed Computing Cloud Computing Containerization Kubernetes Docker GPU Computing Infrastructure Automation Model Deployment Inference Infrastructure Backend Systems High Availability Systems Scalable AI Services Infrastructure Monitoring CloudNative Architecture 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 Cloud Computing Electronics Communication Engineering M.E.M.Tech in relevant disciplines MCA M.Sc. in Computer Science, Artificial Intelligence, Data Science, Information Technology, or related disciplines Any equivalent programme Strong programming skills, operating systems knowledge, networking fundamentals, cloud computing concepts, analytical thinking, and an interest in Artificial Intelligence infrastructure, distributed systems, and backend engineering will be an added advantage but are not mandatory. Perks Internship Certificate Letter of Recommendation (Performance Based) Mentorship from experienced AI, cloud, and infrastructure engineering professionals. Opportunity to contribute to live AI infrastructure, cloud computing, and enterprise platform engineering projects. Exposure to distributed systems, cloud-native architectures, GPU computing, containerization, and scalable AI platforms. Professional development sessions on Artificial Intelligence, cloud engineering, infrastructure automation, DevOps, and software engineering. 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