Director Site Reliability Engineer, AI Infrastructure

At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration. We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution.  Ready to come find your playground? Together, we can help shape the endless possibilities of AI.  Role Overview d-Matrix designs and manufactures purpose-built AI inference silicon, and the infrastructure underpinning our engineering organization must be as reliable and scalable as the chips we build. This role builds and leads d-Matrix's Site Reliability Engineering function from the ground up, owning the infrastructure that development, validation, and customer-facing deployments run on — spanning colocation facilities, on-premises lab clusters, cloud environments (AWS, Azure, GCP), and customer-facing platform services. You will hire and grow the team, set technical direction, own SLOs for critical systems, and serve as the senior escalation point for incidents, all while remaining hands-on. You will partner closely with Software Tools, QA DevOps, and Engineering teams whose workflows run on this infrastructure, as well as Networking Operations and IT Operations, to drive reliability, cost efficiency, and the removal of technical debt. What You Will Do - Build and lead the SRE function from scratch — define its charter, establish SRE as a discipline across hardware and software engineering, and hire, develop, and retain a team of 1–3 SRE engineers. - Direct a dedicated Data Center & Lab Technician team, setting work priorities and operational standards across on-premises and colocation facilities. - 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. - Establish SRE process from zero: define SLIs/SLOs and error budgets, design on-call rotations (including a follow-the-sun model as d-Matrix expands globally), and build the incident and RCA management framework. - Own the full observability stack (Prometheus, Grafana, Datadog, Splunk or equivalent), instrumenting metrics, traces, and logs for SLO visibility and alert design. - Drive IaC-first automation (Terraform, Ansible) and build self-healing infrastructure — host lifecycle automation, fleet auto-remediation, and AIOps-driven alerting. - Own FinOps and capacity planning across cloud, colocation, and on-premises tiers, including spend attribution, TCO modeling, and workload placement decisions. - Lead the migration from ad-hoc JBOD storage to an enterprise-grade shared storage platform spanning on-prem, colocation, and cloud, including architecture, vendor selection, and DR design. What You Will Bring - Bachelor's or Master's in Computer Science, Electrical Engineering, or a related field; 15+ years in SRE, infrastructure engineering, or production engineering. - 5+ years leading SRE or infrastructure engineering teams, including experience building or significantly rebuilding a function, not just managing a steady-state team. - Demonstrated track record of establishing SRE as a discipline in an organization that lacked it: defining SLOs, creating on-call frameworks, standing up observability, and driving cultural change with teams coming from a reactive ops background. - Deep Linux systems expertise: bare-metal operations, enterprise shared storage platforms (NAS/SAN, NFS/SMB at scale, snapshot and replication architectures), and hybrid-cloud storage integration. - Proven experience operating colocation and on-premises hardware at scale: server lifecycle, power and cooling awareness, rack-level networking. - Hands-on Infrastructure as Code fluency with Terraform and Ansible at production scale — module design, remote state, environment isolation, and change governance. - Kubernetes cluster operations: lifecycle management, workload reliability, storage, and RBAC at scale. - Full observability stack ownership (Prometheus, Grafana, Datadog and/or Splunk or equivalent) for SLO definition, alert design, and end-to-end signal quality. - Strong Python and/or Go scripting for production services and automation that safely touches real infrastructure. - Executive communication skills, translating infrastructure health and operational risk into clear narratives for senior leadership, including non-technical stakeholders. - Ability to operate in a high-ambiguity, low-process environment, building structure rather than inheriting it. Preferred Qualifications - Experience operating customer-facing infrastructure or platform services, with reliability expectations beyond in