MLOPS 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 MLOps Engineering Internship provides practical exposure to the deployment, automation, monitoring, and lifecycle management of machine learning systems. Interns will contribute to building scalable MLOps pipelines that enable efficient model development, continuous integration and deployment, model versioning, performance monitoring, infrastructure automation, and reliable AI operations across production environments. Working alongside artificial intelligence, machine learning, software engineering, cloud engineering, DevOps, and research teams, interns will gain handson experience in developing endtoend machine learning operations pipelines, automating model deployment workflows, managing AI infrastructure, and supporting productionready AI systems while strengthening their understanding of enterprise AI engineering practices. Key Responsibilities Assist in developing and maintaining MLOps pipelines. Support machine learning model deployment and lifecycle management. Implement Continuous Integration and Continuous Deployment CICD workflows for AI applications. Assist in model versioning, experiment tracking, and artifact management. Support infrastructure automation for machine learning workloads. Monitor deployed models for performance, reliability, and data drift. Assist in developing automated model retraining and validation workflows. Support cloudbased AI deployment and containerized applications. Prepare technical documentation, operational procedures, and engineering reports. Collaborate with AI, software engineering, cloud engineering, DevOps, and research teams on live projects. Stay updated with advancements in MLOps, AI infrastructure, and production machine learning. Perform additional MLOps engineering assignments as required. Learning Outcomes Interns will gain practical exposure to: Machine Learning Operations MLOps Model Deployment CICD for AI Model Versioning Experiment Tracking Infrastructure Automation Model Monitoring Data Drift Detection Pipeline Automation Containerization Cloud AI Deployment Workflow Orchestration AI Lifecycle Management Production Machine Learning Performance Monitoring AI Infrastructure 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, analytical thinking, cloud computing fundamentals, software engineering practices, and an interest in Artificial Intelligence, machine learning operations, and production 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 Cloud Computing Electronics Communication Engineering Mathematics Statistics Any Relevant Engineering or Science Discipline Perks Internship Certificate Letter of Recommendation (Performance Based) Mentorship from experienced AI, cloud, and software engineering professionals. Opportunity to contribute to live MLOps, machine learning deployment, and AI platform engineering projects. Exposure to production AI systems, deployment pipelines, infrastructure automation, cloud-native technologies, and enterprise engineering practices. Professional development sessions on Artificial Intelligence, MLOps, software engineering, cloud computing, 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