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.

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.

Build AI-driven SRE tooling and agentic automation to triage, diagnose, and remediate infrastructure issues. Integrate LLM-powered agents with observability, ITSM, and infrastructure APIs, develop self-service tooling and ChatOps, tune event/alert intelligence, and convert runbooks into auditable executable automations while contributing to operational standards and incident learning.

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.