MLOps Engineer Internship

Overview: Operationalizing AI and Personalization The MLOps Engineer is crucial for bridging the gap between data science and production, responsible for the reliable, scalable, and secure deployment of machine learning models. You will operationalize the models powering our AI agents and the e-commerce personalization systems, ensuring continuous integration, delivery, and monitoring of our predictive analytics and recommendation engines. Internship Details Duration: 3 months Start Date: Immediate Location: Remote Stipend: None initially. Based on your first-quarter performance, you may be offered a paid full-time opportunity, or even be absorbed directly by the client as an FTE. Key Responsibilities & Core Projects You will build the automation infrastructure that turns static models into continuously improving production systems. Model CI/CD Pipelines: Design and build robust CI/CD pipelines dedicated to the machine learning lifecycle: automated model training, validation, and deployment using tools integrated with our main Makefile CI/CD setup. Model Monitoring & Tracking: Implement comprehensive monitoring and alerting for model performance (e.g., drift detection, prediction accuracy, latency) and track experiments and model artifacts using version control tools. Production Deployment: Operationalize the deployment of ML models powering AI agents and e-commerce services, ensuring they integrate seamlessly into the NestJS modular monolith architecture. Versioning