Requirements

A Master’s degree in Computer Science, Engineering, or a related field.
7+ years of experience in a DevOps or SRE role, with strong expertise in cloud computing and distributed systems.
Hands-on experience with site reliability issues, including root cause analysis, in-production troubleshooting, and on-call rotations.
Proficiency in working with reliability KPIs, such as observability, alerting, and SLAs.
Experience with CI/CD, containerization, and orchestration tools like Docker and Kubernetes.
Knowledge of monitoring, logging, alerting, and observability tools such as Prometheus, Grafana, ELK Stack, or Datadog.
Familiarity with infrastructure-as-code tools like Terraform or CloudFormation.
Proficiency in scripting languages (Python, Go, Bash) and a strong understanding of software development best practices.
Solid grasp of networking, security, and system administration concepts.
Excellent problem-solving and communication skills, with the ability to work effectively in a collaborative environment.
Experience in an AI/ML environment, high-performance computing (HPC) systems, or modern AI-oriented solutions (e.g., Fluidstack, Coreweave, Vast) is a plus.

What You'll Be Doing

Design, build, and maintain scalable, highly available, and fault-tolerant infrastructures to support web services and ML workloads.
Ensure our platform, inference, and model training environments are always highly available and enable seamless replication across HPC clusters.
Operate systems and troubleshoot issues in production, including interrupts, on-call responses, and infrastructure scaling.
Implement and improve monitoring, alerting, and incident response systems to minimize downtime and optimize performance.
Develop and maintain workflows and tools for CI/CD, containerization, orchestration, monitoring, and logging.
Participate in on-call rotations to respond to incidents and perform root cause analysis.
Drive continuous improvement in infrastructure automation, deployment, and orchestration using tools like Kubernetes, Flux, and Terraform.
Collaborate with AI/ML researchers to enable safe and reproducible model-training experiments.
Build a cloud-agnostic platform that abstracts infrastructure complexities for science and engineering teams.
Design and develop new workflows, tooling, and automation to improve system reliability, availability, and performance.
Work with the security team to ensure infrastructure adheres to best practices and compliance requirements.
Document processes and procedures to ensure consistency and knowledge sharing across the team.

We offer a comprehensive benefits package designed to support well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.