Senior DevOps / MLOps Engineer

Salary Range: 170000 to 260000 (Currency: USD) (Pay period: per-year-salary)

Key Responsibilities

• Manage and maintain infrastructure, including Kubernetes GPU resource scheduling, scaling, monitoring, logging, and performance optimization.
• Develop and maintain automated machine learning pipelines using tools such as Kubeflow and Ray.
• Manage and support collaboration and productivity platforms, including Google Workspace, GitLab, and related CI/CD integrations.
• Implement and enforce cybersecurity policies and best practices to safeguard sensitive data and systems.
• Provide technical support to team members by troubleshooting hardware, software, and network issues.

Qualifications

• 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.

Preferred Qualifications

• 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.