This role focuses on automating processes, building and implementing robust observability solutions, and ensuring seamless operations for mission-critical AI infrastructure.

The ideal candidate will combine strong coding abilities with hands-on data center experience to build scalable reliability services, optimize system performance, and minimize downtime,including close partnership with facility operations to address physical infrastructure impacts.

In an era where AI workloads demand near-zero downtime, this position plays a pivotal role in bridging software engineering principles with physical data center realities.

By prioritizing automation and observability, team members in this role can reduce mean time to recovery (MTTR) by up to 50% through proactive monitoring and automated remediation, based on industry benchmarks from high-scale environments like those at hyperscale cloud providers.

Responsibilities:

• Design, develop, and deploy scalable code and services (primarily in Python and Rust, with flexibility for emerging languages) to automate reliability workflows, including monitoring, alerting, incident response, and infrastructure provisioning.

• Implement and maintain observability tools and practices, such as metrics collection, logging, tracing, and dashboards, to provide real-time insights into system health across multiple data centers,open to innovative stacks beyond traditional ones like ELK.

• Collaborate with cross-functional teams,including software development, network engineering, site operations, and facility operations (critical facilities, mechanical/electrical teams, and data center infrastructure management),to identify reliability bottlenecks, automate solutions for fault tolerance, disaster recovery, capacity planning, and physical/environmental risk mitigation (e.g., power redundancy, cooling efficiency, and environmental monitoring integration).

• Troubleshoot and resolve complex issues in data center environments, including hardware failures, environmental anomalies, software bugs, and network-related problems, while adhering to reliability principles like error budgets and SLAs.

• Optimize Linux-based systems for performance, security, and reliability, including kernel tuning, container orchestration (e.g., Kubernetes or emerging alternatives), and scripting for automation.

• Understand network topologies and concepts in large-scale, multi-data center environments to effectively troubleshoot connectivity, routing, redundancy, and performance issues; integrate observability into data center interconnects and facility-level controls for rapid diagnosis and automation.

• Participate in on-call rotations, post-incident reviews (blameless postmortems), and continuous improvement initiatives to enhance overall site reliability, including joint exercises with facility teams for physical failover and recovery scenarios.

• Mentor junior team members and document processes to foster a culture of automation, knowledge sharing, and adaptability to new technologies.

Basic Qualifications:

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a closely related technical field (or equivalent professional experience).

• 5+ years of hands-on experience in site reliability engineering (SRE), infrastructure engineering, DevOps, or systems engineering, preferably supporting large-scale, distributed, or production environments.

• Strong programming skills with proven production experience in Python (required for automation and tooling); experience with Rust or willingness to work in Rust is a plus, but strong coding fundamentals in at least one systems-level language (e.g., Python, Go, C++) are essential.

• Solid experience with Linux systems administration, performance tuning, kernel-level understanding, and scripting/automation in production environments.

• Practical knowledge of containerization and orchestration technologies, such as Docker and Kubernetes (or similar systems).

• Experience implementing observability solutions, including metrics, logging, tracing, monitoring tools (e.g., Prometheus, Grafana, or alternatives), alerting, and dashboards.

• Familiarity with troubleshooting complex issues in distributed systems, including software bugs, hardware failures, network problems, and environmental factors.

• Understanding of networking fundamentals (TCP/IP, routing, redundancy, DNS) in large-scale or multi-site environments.

• Experience participating in on-call rotations, incident response, post-incident reviews (blameless postmortems), and reliability practices such as error budgets or SLAs.

• Ability to collaborate effectively with cross-functional teams (software engineers, network teams, site/facility operations, mechanical/electrical teams).

Preferred Skills and Experience:

• 7+ years of experience in SRE or infrastructure roles, ideally in hyperscale, cloud, or AI/ML training infrastructure environments with multi-data center setups.

• Hands-on experience operating or scaling Kubernetes clusters (or equivalent orchestration) at large scale, including automation for provisioning, lifecycle management, and high-availability.

• Proficiency in Rust for systems programming and performance-critical components.

• Direct experience integrating software reliability tools with physical data center infrastructure.

• Experience with observability tools and practices, such as metrics collection, logging, tracing, and dashboards.

• Familiarity with containerization and orchestration technologies, such as Docker and Kubernetes (or similar systems).

• Experience with Linux systems administration, performance tuning, kernel-level understanding, and scripting/automation in production environments.

• Understanding of networking fundamentals (TCP/IP, routing, redundancy, DNS) in large-scale or multi-site environments.

• Experience participating in on-call rotations, incident response, post-incident reviews (blameless postmortems), and reliability practices such as error budgets or SLAs.

• Ability to collaborate effectively with cross-functional teams (software engineers, network teams, site/facility operations, mechanical/electrical teams).

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