Senior DevOps Engineer
Palo Alto, CA
⚠️ We'll shut down after Aug 1st - try foo🦍 for all jobs in tech ⚠️
GenBio AI
GenBio AI pioneers foundation models for biology, transforming drug design, bioengineering, and personalized medicine. Advancing vaccine safety, treatment personalization, and clinical diagnostics with cutting-edge generative biology research.Responsibilities
- You will continue to develop and empower a diverse team of developers, providing technical guidance and direction and anticipating future resource needs in line with business goals and priorities
- Develop and implement a scalable technical strategy to address complex challenges.
- Establish technical standards and enhance the developer experience across the organization.
- Define team goals, drive execution, and manage cloud infrastructure (GCP, Docker, Terraform, etc.).
- Develop reliability and observability strategies to ensure system performance and resilience.
- Build tools and processes for effective operational and software development management.
- Collaborate on team strategy, set goals, and provide technical direction while fostering growth.
- Partner with cross-functional teams (science, engineers, and operations) to align initiatives.
- Contribute to core decisions on tools, infrastructure, and architectural design.
- Build and operate processes and tools for effective operational management of research and production software and to enable and enhance effective software development practices.
Qualifications
- Cloud Infrastructure: Proven experience in building, deploying, and maintaining production systems on major cloud platforms, with a preference for Google Cloud Platform (GCP).
- Containerization and Orchestration: Proficiency with Docker and Kubernetes/KubefLow for containerization and orchestration, ensuring efficient deployment and scaling of applications.
- Infrastructure as Code (IaC): Strong understanding of IaC principles, utilizing tools like Terraform to manage and provision cloud resources.
- Continuous Integration/Continuous Deployment (CI/CD): Experience with CI/CD pipelines and release management, employing tools such as GitLab CI, GitHub Actions, or CircleCI to automate testing and deployment processes.
- Programming Skills: Advanced proficiency in Python programming, complemented by experience in shell scripting, enabling effective automation and integration tasks.
- Deployment Tools: Familiarity with deployment tools like Helm, Kustomize, Kubeflow or Kapitan to manage Kubernetes configurations and streamline application releases.
- Monitoring and Observability: Experience with monitoring and observability tools such as Grafana, Prometheus, or Splunk to ensure system reliability and performance.
- Security Awareness: Ability to identify security risks within cloud infrastructures and implement measures to mitigate them, ensuring robust and secure systems.
- Platform Development: Demonstrated experience in building secure and scalable platforms or products in cloud environments, emphasizing best practices in architecture and design.
- Site Reliability Engineering (SRE) and DevOps Practices: Practical experience with modern SRE and DevOps lifecycles, tools, and frameworks, fostering a culture of automation and continuous improvement.
- Adaptability and Hands-on Approach: Pragmatic and flexible, willing to engage directly in technical tasks when necessary to achieve team objectives
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.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Architecture Biology Chemistry CI/CD Computer Science DevOps Docker Engineering GCP GitHub GitLab Google Cloud Grafana Helm Kubeflow Kubernetes Machine Learning MLOps Pharma Pipelines Python Research Security Shell scripting Splunk Terraform Testing
Perks/benefits: Career development Startup environment
More jobs like this
Explore more career opportunities
Find even more open roles below ordered by popularity of job title or skills/products/technologies used.