Senior Technical Lead

Role purpose

Cluster architecture and operations

• Design and provision production-grade clusters on bare-metal or virtualized environments, including highly available control planes, etcd health, and node pools.
• Bootstrap clusters securely in connected and air-gapped environments using private registries, image mirrors, and OS hardening.
• Configure CNI networking, ingress, load balancing, DNS, IPv4/IPv6, and Multus when needed.
• Plan upgrades, patching, capacity management, autoscaling, right-sizing, multi-tenancy, and node lifecycle automation.
• Maintain disaster recovery, backups, restores, documented runbooks, and regular recovery tests.

AI/ML, storage, and security

• Enable GPU access for workloads in each Kubernetes deployment; operate model serving, batch and streaming pipelines, and ML orchestration.
• Optimize AI/ML workloads for throughput, latency, and GPU utilization.
• Manage CSI drivers, dynamic storage provisioning, storage integrations, and stateful workloads with suitable SLAs.
• Enforce RBAC, pod security, network policies, image scanning and signing, admission policies, secrets management, enterprise identity, certificates, and audit logging.

Platform engineering and reliability

• Standardize delivery with Helm and Kustomize; implement GitOps and infrastructure as code for repeatable environments.
• Operate on-premises CI/CD runners and artifact registries, including air-gapped workflows.
• Implement metrics, logs, tracing, service-level indicators and objectives, and capacity and health dashboards.
• Lead incident response, root-cause analysis, and continuous improvement; participate in an on-call rotation.

Education and experience

• Bachelor’s degree in Computer Science, Engineering, Data/AI, or a related field, or equivalent experience; a Master’s degree is preferred.
• The description states 7–10 years in platform/SRE/DevOps/systems roles, including 4+ years administering production Kubernetes.
• It also states 8+ years in software/ML engineering and 3+ years building GenAI/LLM solutions and production services.
• Proven experience designing, building, and operating on-premises Kubernetes clusters in data-center environments without managed services.
• Hands-on experience with GPUs for AI workloads, air-gapped operations, private registries, and enterprise identity/security integrations.
• CKA is required or must be obtained within six months; CKS/CKAD are preferred, and relevant Linux/networking certifications are a plus.
• The description also calls for on-premises or air-gapped deployments, Kubernetes, service meshes, secure networking, and data engineering at scale.

Technical skills

• Kubernetes core operations include control planes, etcd, scheduling, admission controllers, multi-tenancy, namespaces, quotas, pod disruption budgets, affinities, and taints/tolerations.
• Linux and containers include containerd or CRI-O, systemd, kernel and GPU drivers, filesystems, and OS hardening.
• Networking includes Cilium or Calico, network policies, ingress/egress, and optionally Istio or Linkerd.
• Storage includes CSI, persistent volumes and claims, snapshots, performance tuning, backup/restore, and disaster recovery; Ceph/Rook/NetApp/NFS familiarity is noted.
• Security, observability, and delivery tools include RBAC, OPA/Kyverno, Vault/Sealed Secrets, OIDC/LDAP/AD, Prometheus/Grafana, EFK/ELK/Loki, OpenTelemetry, Helm/Kustomize, Argo CD/Flux, Terraform/Ansible, CI tools, Bash, Python, and YAML.

Collaboration