MLOPS INTERN

About the program EduRankAI invites applications for the position of MLOps Intern to join its Machine Learning Operations MLOps and AI Infrastructure Engineering team. This fulltime internship is designed for students and recent graduates who are passionate about Machine Learning Operations, Artificial Intelligence, Cloud Computing, DevOps, AI Infrastructure, Model Deployment, Automation, and scalable production systems. As an MLOps Intern, you will contribute to building, deploying, monitoring, and maintaining reliable Machine Learning pipelines that power EduRankAI's AI Tutor, intelligent educational platforms, recommendation systems, predictive analytics, and AIdriven research initiatives. You will work closely with Machine Learning Engineers, AI Researchers, Platform Engineers, and DevOps teams to bridge the gap between model development and production deployment. As an MLOps Intern, you will collaborate with AI engineers, data scientists, platform engineers, DevOps engineers, and software development teams to automate the lifecycle of machine learning models. Your responsibilities will include deploying machine learning models into staging and production environments building automated CICD pipelines for AI applications containerizing machine learning services using Docker assisting with Kubernetesbased deployments implementing model monitoring, drift detection, and performance tracking solutions automating model retraining and validation workflows maintaining experiment tracking and model versioning systems supporting Infrastructure as Code IaC initiatives monitoring cloud infrastructure and production AI services troubleshooting deployment issues documenting deployment procedures, operational workflows, and engineering standards and collaborating with multidisciplinary teams to improve the scalability, reliability, security, and operational efficiency of production AI systems. Applicants should possess a strong understanding of Python programming, software engineering principles, machine learning fundamentals, Linux systems, cloud computing concepts, networking basics, and DevOps practices. Familiarity with Docker, Kubernetes, Git, CICD platforms, MLflow, Weights Biases WB, DVC, Airflow, Terraform, FastAPI, REST APIs, cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform GCP, monitoring tools such as Prometheus and Grafana, model serving frameworks, Infrastructure as Code IaC, model versioning, feature stores, or distributed systems will be considered an advantage but is not mandatory. Knowledge of Machine Learning pipelines, MLOps lifecycle, model monitoring, data drift detection, experiment tracking, API deployment, container orchestration, or AI infrastructure engineering will also be beneficial. Candidates with academic projects, hackathons, opensource contributions, AI competitions, research work, or personal projects involving cloud infrastructure, DevOps, Machine Learning deployment, or production AI systems are strongly encouraged to apply. Throughout the internship, participants will receive structured mentorship, continuous technical guidance, architecture reviews, code reviews, engineering best practices, and exposure to modern MLOps workflows used in production environments. Interns will gain practical experience in AI deployment, model lifecycle management, CICD automation, containerization, cloud infrastructure, monitoring, observability, technical documentation, collaborative engineering, and productiongrade AI operations while contributing directly to live Artificial Intelligence initiatives. We are looking for individuals with strong analytical ability, logical reasoning, engineering discipline, problemsolving skills, attention to detail, curiosity, communication skills, ownership, adaptability, teamwork, operational excellence, research orientation, 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 MLOps experience, professional portfolio development, and opportunities for outstanding performers to be considered for advanced AI engineering 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 MLOps Engineers and AI Infrastructure Engineers Hands-on experience with production-grade Machine Learning deployment and AI infrastructure Exposure to Docker, Kubernetes, CI/CD, cloud platforms, and model monitoring Opportunity to contribute to live Artificial Intelligence and Machine Learning production systems Professional MLOps portfolio development Cross-functional collaboration with AI research, Machine Learning, DevOps, software engineering, and platform teams Performance-based Pre-Placement Interview opportunity Exposure to modern MLOps workflows, cloud-native AI infrastructure, automation, and production engineering practices 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 Machine Learning Operations and AI Infrastructure projects while maintaining professionalism, confidentiality, documentation standards, engineering best practices, operational discipline, security practices, and project timelines. Participants will collaborate with multidisciplinary teams, deploy and monitor machine learning models, automate AI workflows, improve cloud infrastructure, prepare technical documentation, participate in engineering reviews, and support continuous improvements in production-ready AI systems. Successful completion of the internship will be evaluated based on technical competency, engineering quality, automation effectiveness, documentation standards, collaboration, ownership, consistency, professionalism, and overall contribution throughout the internship. Number of openings 10