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Design template controls packages, sequences of operations, points lists, and BOMs for modular data centers. Own Division 25 automation spec, coordinate controls across mechanical/electrical/commissioning teams, and produce repeatable, deployable controls designs for programming and field implementation.

Design end-to-end datacenter network architectures for AI training and inference: topology, IP/routing, RDMA/lossless fabrics, physical integration (rack, power, cabling), and comprehensive HLD/LLD documentation. Collaborate with hardware, operations, cabling, software, and validation teams to produce deployable, scalable, and performance-validated designs.

Design and build end-to-end software primitives for AI infrastructure: domain modeling of power/datacenters/chips, authorization, audit, orchestration, vendor data ingestion (KMZ/BIM/Revit), and agent integration to enable safe, auditable agent operators. Ship production-quality systems and work across ambiguity to turn unstructured data into durable, composable entities.

Deploy, validate, and support data center network infrastructure: configure switches, install and test optics and fiber (OTDR/insertion loss), manage hardware logistics, maintain as-builts/cutsheets, and collaborate with cross-functional teams to drive on-site deployments and post-deployment incident response. Frequent travel for onsite turn-ups.

Build and maintain foundational infrastructure platforms (CMDB, DCIM, asset lifecycle, monitoring, observability, automation). Deliver self-service tooling, workflow orchestration, integrations, and scalable, reliable systems while partnering with operations, security, and product teams. Drive technical direction, CI/CD, IaC, and production readiness for fleet-scale data center infrastructure.

Design, build, and operate the observability and control-plane for a hyperscale GPU fleet. Own data pipelines, API surface, fleet state as source-of-truth, distributed command execution, and clean hardware/site onboarding (ZTP/DHCP/DNS). Ensure production reliability, run incidents, and drive automation to eliminate toil.

Design and own core backend primitives (entities, authz, audit, orchestration), model datacenter and hardware domains, ingest vendor/geospatial/BIM formats, build durable workflow and agent platforms, and enable AI agents to operate systems safely with strong guardrails and audit trails.

Build Fluidstack's AI-integrated software stack end-to-end: supply chain automation, capacity forecasting, internal AI tooling and agent frameworks, operational automation, and datacenter design/validation. Ship features from concept to production with high ownership and measurable outcomes.

Build and operate the observability platform, data pipelines, and production control plane for a hyperscale GPU fleet. Define infrastructure API surfaces, ensure fleet state is source of truth, integrate new hardware (ZTP/DHCP/DNS), and maintain Kubernetes-based infrastructure and SLOs. Own incidents, postmortems, and system reliability.

Build and operate large-scale GPU compute fleet tooling: metrics pipelines, alerting, Redfish/BMC telemetry, repair/RMA automation, GPU qualification, and orchestration for Kubernetes and bare-metal deployments. Own reliability, scalability, incident response, and automation to minimize manual toil.