AI INFRASTRUCTURE INTERN

About the program EduRankAI invites applications for the position of AI Infrastructure Intern to join its Artificial Intelligence Infrastructure and Platform Engineering team. This fulltime internship is designed for students and recent graduates who are passionate about Artificial Intelligence, Machine Learning Infrastructure, MLOps, Cloud Computing, DevOps, Platform Engineering, AI Operations, and scalable research infrastructure. As an AI Infrastructure Intern, you will contribute to building and maintaining the infrastructure that powers AI research, model development, experiment management, and production AI systems across the EduRankAI ecosystem. Your work will directly support researchers and engineers by improving the efficiency, reproducibility, scalability, and reliability of AI experimentation and deployment workflows. As an AI Infrastructure Intern, you will collaborate with AI researchers, machine learning engineers, platform engineers, DevOps professionals, and software engineers to develop and optimize AI infrastructure components. Your responsibilities will include supporting the development and maintenance of machine learning training pipelines, assisting in configuring experiment tracking platforms, improving reproducibility of AI experiments, managing compute scheduling for training and inference workloads, supporting GPU resource allocation, monitoring infrastructure performance and utilization, optimizing infrastructure costs, automating operational workflows, maintaining containerized development environments, assisting with cloudbased AI infrastructure, documenting infrastructure architecture, preparing troubleshooting guides and operational runbooks, supporting CICD workflows for AI projects, monitoring system health, and collaborating with multidisciplinary teams to improve AI development efficiency and platform reliability. Applicants should possess a strong understanding of Python programming, software engineering principles, Linux fundamentals, cloud computing concepts, operating systems, networking basics, and machine learning workflows. Familiarity with MLflow, Weights Biases WB, Docker, Kubernetes, Git, CICD pipelines, cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform GCP, Infrastructure as Code IaC, GPU computing, experiment tracking, distributed computing, REST APIs, MLOps tools, or workflow orchestration platforms will be considered an advantage but is not mandatory. Knowledge of PyTorch, TensorFlow, containerization, virtualization, scripting, monitoring tools, logging systems, or infrastructure automation will also be beneficial. Candidates with academic research, opensource contributions, hackathons, cloud projects, or personal projects involving machine learning infrastructure, DevOps, cloud engineering, or MLOps are strongly encouraged to apply. Throughout the internship, participants will receive structured mentorship, continuous technical guidance, code reviews, engineering best practices, and exposure to modern AI infrastructure and platform engineering workflows. Interns will gain practical experience in AI infrastructure management, MLOps, experiment tracking, cloud platforms, automation, infrastructure monitoring, compute resource management, containerization, technical documentation, collaborative engineering, and production AI operations while contributing directly to live Artificial Intelligence research and engineering initiatives. We are looking for individuals with strong analytical ability, logical reasoning, engineering discipline, problemsolving skills, attention to detail, communication skills, adaptability, ownership, teamwork, curiosity, and a continuous learning mindset. The internship is primarily conducted onsite at the EduRankAI campus with structured mentorship and collaborative engineering activities. Remote participation may be permitted only in exceptional, preapproved cases. Applications submitted under EduRankAI's Talent Accessibility Initiative are completely free. This is an unpaid internship intended to provide meaningful industry exposure, structured mentorship, handson AI infrastructure experience, professional portfolio development, and opportunities for outstanding performers to be considered for advanced infrastructure projects, internship extensions, preplacement interviews, research collaborations, or future fulltime opportunities based on performance and organizational requirements. Perks Certificate of Completion Letter of Recommendation for exceptional performers Mentorship from Senior AI Infrastructure Engineers and Platform Engineers Hands-on experience with production AI infrastructure and MLOps workflows Exposure to cloud platforms, experiment tracking, GPU computing, and AI operations Opportunity to contribute to live AI research infrastructure and platform engineering projects Professional AI infrastructure portfolio development Cross-functional collaboration with AI research, engineering, DevOps, and platform teams Performance-based Pre-Placement Interview opportunity Exposure to cutting-edge MLOps, cloud infrastructure, automation, and AI platform technologies 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 internship requiring a commitment of six days per week, for a duration of three months. The internship is primarily conducted on-site at the EduRankAI campus with structured mentorship and collaborative engineering activities. Remote participation may be permitted only in exceptional cases with prior organizational approval. Interns will contribute to live AI infrastructure and platform engineering projects while maintaining professionalism, confidentiality, documentation standards, engineering best practices, operational discipline, and project timelines. Participants will collaborate with multidisciplinary teams, support machine learning infrastructure, maintain experiment tracking systems, optimize compute resources, prepare technical documentation, participate in infrastructure reviews, and contribute to continuous improvements in AI platform reliability and scalability. Successful completion of the internship will be evaluated based on technical competency, engineering quality, analytical ability, documentation standards, collaboration, ownership, consistency, professionalism, and overall contribution throughout the internship. Number of openings 10