Contract Lead, Site Reliability Engineering — AI Accelerator Infrastructure
Leadership & Organizational Build-Out
You will build and lead d-Matrix’s site reliability engineering function from the ground up—owning the infrastructure that development, validation, and customer-facing deployments run on. This spans colocation facilities, on-premises lab clusters, cloud environments (AWS, Azure, and GCP), and the platform services customers use to collaborate with d-Matrix on hardware and software deployments.
You are a hands-on engineering leader. You will establish SRE as a discipline at d-Matrix; hire and grow the team; set the technical direction; own SLOs for critical systems; and be the senior escalation point when things go wrong—all in parallel.
You will partner closely with the director of DevOps engineering, whose pipelines and automation run on the infrastructure you own, and work directly with hardware and software development teams to ensure HPC infrastructure meets their workload requirements.
Reliability & Observability — Building From Scratch
Establish SRE processes from a zero baseline: define SLIs and SLOs, build error budgets, design on-call rotations, and create the incident management framework d-Matrix currently lacks.
Own 24×7 reliability across colocation, on-premises lab clusters, cloud, and customer-facing platform services — designing for failure domains, progressive delivery, and strict change control at every tier.
Own the full observability stack (metrics, traces, and logs) and instrument it from the ground up—Prometheus, Grafana, and/or Datadog—with SLO visibility, alert design, and E2E signal quality.
Evolve incident and problem management into a data-driven discipline: automated triage workflows, pattern detection across recurring failures, and every P0/P1 producing a written RCA with tracked systemic fixes.
Own FinOps and capacity planning as a unified discipline across all three infrastructure tiers—cloud (AWS, Azure, GCP), colocation, and on-premises: establish spend visibility and attribution across every tier, model TCO comparatively, drive workload placement decisions based on cost and performance, and anticipate infrastructure needs for new silicon programs and customer deployments.
Migrate from ad hoc JBOD-based storage and point-in-time snapshots to an enterprise-grade shared storage platform spanning on-premises, colocation, and cloud tiers—covering architecture, vendor selection, data protection design (snapshots, replication, DR), and integration with HPC workloads and development environments.
Automation & Infrastructure as Code — Establishing the Baseline
There is no automation baseline today. You will build it. Drive IaC-first discipline across the team—Terraform, Ansible, and production-quality automation for all infrastructure provisioning and lifecycle management; this capability is currently absent, and you will establish it.
Build self-healing infrastructure platforms: host lifecycle automation, fleet auto-remediation, and AIOps-driven alerting that reduce manual intervention across the operational lifecycle.
Instrument the team’s own development practices—runbooks, change governance, and deployment pipelines for infrastructure code—establishing standards that scale as the team grows.
Documentation & Global Collaboration
Build a documentation culture from scratch: runbooks, architecture diagrams, and operational playbooks maintained as living artifacts—not a one-time project.
Design and scale a follow-the-sun on-call model as d-Matrix expands globally; the framework you build now will be the foundation the team inherits.
Drive POC and POV evaluations for new infrastructure technologies, interconnect fabrics, and platform services relevant to d-Matrix’s accelerator roadmap.