Senior DevOps Engineer

TetraScience is the Scientific Data and AI Cloud company with a mission to radically improve and extend human life. They combine the world's only open, purpose-built, and collaborative scientific data and AI cloud with deep scientific expertise across the value chain to accelerate and improve scientific outcomes.

As a Senior DevOps Engineer at TetraScience, you will:

• Collaborate with product and engineering teams to drive and enhance the entire lifecycle of products
• Deploy and troubleshoot products in clients' AWS environments
• Develop infrastructure-as-code (IaC) software layers, scripts, deployment frameworks, monitors, and self-healing tools
• Assist in building accurate monitoring and metrics systems for applications
• Manage internal AWS environments and network
• Participate in meetings with potential clients to address integration questions
• Maintain up-to-date documentation on deployments, processes, and standard operating procedures

Requirements:

• Bachelor's degree in Computer Science, Engineering, or related field; or equivalent experience
• At least 5 years of professional experience as a DevOps Engineer or similar role
• Extensive experience with Amazon Web Services (AWS), including AWS certifications
• Strong proficiency in infrastructure-as-code (IaC) tools, preferably CloudFormation
• Proficiency in at least one programming language (Python, Java, or Go)
• Expertise in automating cloud-native technologies and deploying applications
• Experience with cloud-native CI/CD workflows and tools
• Familiarity with microservices architecture, containerization, and orchestration tools
• Solid understanding of Linux basics, networking fundamentals, and security best practices
• Excellent communication skills

TetraScience offers a collaborative environment with core values focused on transparency, trust, fearlessness, customer alignment, commitment to craft, and equality of opportunity. Join their team to be part of the Scientific AI revolution and work on cutting-edge technologies in the scientific data management field.