Responsibilities

📍Build and extend tooling for end-to-end ML model deployment and lifecycle management 📍Setup, configure and connect cloud infrastructure services together to serve as the foundation of our platform 📍Automate deployment orchestration, building a fast and maintainable CI/CD pipeline for our web applications 📍Hook up real time monitoring and alerting for all parts of the web platform, enabling engineering teams to quickly respond to incidents. 📍Build and maintain analytics pipeline, connecting data sources to data warehouse, then from data warehouse to reporting platform and back to model training. 📍Collaborate with cross-functional teams to deploy and maintain AI models in production environments, ensuring scalability, reliability, efficiency and robustness 📍Orchestrate model serving to accommodate our unique infrastructure in a scalable manner 📍Configure and maintain Kubernetes clusters on Ubuntu. 📍Maintain backend planning and optimize GPU capacity continuously.

Qualifications

📍Bachelor's or Master's degree in Computer Science, a related field, or equivalent work experience 📍5+ years of professional experience as DevOps, TechOps, or SRE engineer 📍Extensive experience with setting up IaaS cloud platforms (GCP preferred) 📍Experience scaling infrastructure for consumer facing web applications 📍Proven experience in working with and scaling GPUs 📍Proficiency in containerization technologies, especially Docker and Kubernetes 📍Proficient in Python and creating scripts to automate pipelines and processes 📍Extensive Linux troubleshooting experience 📍Excellent problem-solving and analytical thinking skills, with a strong attention to detail 📍Effective verbal and written communication a must. 📍Comfortable working in a dynamic, fast-paced, and collaborative environment