Jobs at Carbon3.ai

Serve as the technical bridge between customers and internal teams to design, validate, and onboard AI infrastructure solutions across bare metal, BMaaS, Kubernetes and cloud-native platforms. Lead technical discovery, POCs, benchmarks, architecture reviews, reusable reference architectures, and customer workshops while influencing product roadmap and production readiness.

Own and improve day-to-day procurement operations, including supplier onboarding, due diligence, approvals, renewals, contract records, governance, reporting, and process automation. Partner with Legal, Finance, and internal teams to manage supplier performance, maintain accurate records, mitigate third-party risks, and scale procurement processes. The role also involves implementing supplier management tools, supporting audit readiness, and developing team members.

Own and improve supplier onboarding, supplier records, documentation, contract renewal tracking, and audit readiness. Maintain corporate and production asset registers from purchase through disposal, coordinating with IT, Tech, Finance, and internal stakeholders. Support GRC activities by providing evidence for SOC 2 and ISO 27001 third-party controls. The role requires strong procurement and supplier management experience, process ownership, contract interpretation, attention to detail, and comfort building workflows in a fast-growing startup.

Designs, implements, and operates Era4’s enterprise observability platform across AI infrastructure. Responsibilities include managing telemetry collection, Grafana-based monitoring, Kubernetes integrations, alerting, dashboards, distributed tracing, multi-tenant controls, object-storage-backed retention, and OpenTelemetry integrations. The role also develops Infrastructure-as-Code and GitOps automation, supports incident detection and root-cause analysis, validates resilience and disaster recovery, and establishes observability standards, SLIs, SLOs, security controls, and operational governance.

Serves as the dedicated technical partner for customers using Era4’s AI and HPC infrastructure. Responsibilities include onboarding, workload and capacity planning, cluster and network readiness, technical documentation, SLA and KPI reporting, incident escalation, root cause analysis, governance, and roadmap input. The role requires customer-facing technical experience, Linux troubleshooting, GPU infrastructure knowledge, data-centre networking, and familiarity with ITIL-aligned service processes.

Design, implement, and improve security controls across Kubernetes, cloud-native, and datacentre infrastructure. Secure networks and firewalls, integrate DevSecOps controls into CI/CD, lead vulnerability management, build detection/response capabilities, automate security workflows, and support incident response and threat hunting for GPU-accelerated and AI/ML platforms.

Designs and implements cloud, bare-metal, and Kubernetes-based AI infrastructure across networking, compute, GPU, and storage. Develops scalable automation and production services, models database schemas, creates technical documentation, defines integration tests, and delivers infrastructure components into production. Requires strong experience with cloud-native architecture, Infrastructure as Code, GitOps, software development, and version control.

Design and govern the end-to-end architecture of Era4’s Kubernetes-based AI infrastructure. The role develops platform blueprints, technical standards, high- and low-level designs, multi-tenant GPU cloud architectures, capacity plans, and technology roadmaps. It coordinates Kubernetes, GPU, networking, storage, automation, security, observability, billing, and cloud services while guiding platform engineering and operations teams toward secure, scalable, reliable, and commercially viable solutions.

Design, deploy, operate, and continuously improve Kubernetes-based AI infrastructure. Responsibilities include managing Linux systems, bare-metal servers, data-centre infrastructure, NVIDIA GPU workloads, infrastructure automation, security access, platform governance, troubleshooting, documentation, customer and vendor collaboration, and on-call operational support.

Design, deploy, and operate large-scale GPU-accelerated HPC and AI clusters. Manage scheduling (Slurm), networking (InfiniBand/Ethernet), provisioning, automation, monitoring, incident response, security/RBAC, and vendor engagement to ensure high availability and performance for AI workloads.