Senior DevOps / ML Ops Engineer

Bachelor's degree in Computer Science, Information Technology, or a related field 3+ years of experience in Kubernetes administration and development Hands-on experience in managing machine learning model lifecycles, model serving, and distributed training Proven experience in automation and strong systems engineering skills Solid understanding of cybersecurity principles and best practices Strong sense of ownership and responsibility, with the ability to handle urgent operational issues to ensure smooth team development and deployment MLOps Expertise: Proficiency in MLOps frameworks and tools, such as ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC) Domain Knowledge: Basic understanding of biology or chemistry; experience in the pharmaceutical or biotech industry is a plus Software Engineering Background: Strong foundation in software engineering, with a transition into technical operations roles Security Awareness: Ability to identify security risks and implement effective mitigation measures Start-up Experience: Demonstrated experience in start-up environments, showcasing adaptability and problem-solving skills Production Support: Experience supporting both production systems and machine learning pipelines Educational: Master's/Ph.D. in Computer Science, a related technical field, or equivalent practical experience Leadership Skills: Proven ability to lead and develop engineering teams of 3-5 members or more Saxon Global