Vacancy description
Consulting Pandits
India, Pune
Platform Engineer - Cloud Infrastructure in Pune, India is listed on Jobeax. Browse 30,000+ vacancies available.
Key Responsibilities :- Build and operate Kubernetes clusters, with cloud-hosted control planes and AI accelerator nodes joined as workers over site-to-site connectivity.- Register, label and taint accelerator worker nodes so that inference workloads schedule onto the correct hardware class and manage device scheduling and topology constraints.- Plan and execute cluster and operating system upgrades: RKE2 version upgrades, RHEL patching and major-version migration, etcd backup and restore, and control-plane node replacement.- Own cluster networking and storage end to end: CNI, ingress, DNS, load balancing, CSI drivers, persistent volume lifecycle, backup and tested disaster recovery.- Deploy, configure and upgrade the vendor AI platform stack, which is delivered as Helm charts from an OCI registry and must be installed in a defined dependency order.- Manage platform configuration as code: Helm values files, chart versions, namespace layout, registry pull secrets, artifact credentials and service-account key rotation.- Manage TLS certificates and DNS for the inference API and console endpoints, including CA-issued and wildcard certificates and automated renewal.- Operate the supporting data services the stack depends on, including operator-managed PostgreSQL, Redis queues and the bundled identity provider.- Design and operate cloud network infrastructure: virtual networks, subnets, routing, security groups, NAT and controlled egress, with ongoing cost analysis and right-sizing.- Own our side of IPSec connectivity into the accelerator racks, including tunnel endpoints, client-side routing and failover, and keep hybrid path latency inside inference latency budgets.- Build and maintain Terraform modules and Ansible automation, and reconcile cluster and platform state from version control through a GitOps workflow.- Implement cloud IAM, Kubernetes RBAC, namespace isolation, pod security standards, secrets rotation and hardening baselines, and produce evidence for security reviews.- Deploy and operate the monitoring and logging stack, define service-level objectives and alerts tied to inference availability and latency, and track cluster and accelerator capacity.- Support model bundle and deployment configuration changes through the platform's Kubernetes custom resources, in coordination with ML systems engineers.- Lead incident response for cluster and platform faults, write root-cause analyses that result in a tracked change, and maintain runbooks as a deliverable of each https://jobeax.com/link/8s1JU2HRuPVIpaSw Requirements :- Strong Linux administration on enterprise distributions, at the level of diagnosing service, storage, network, and kernel problems without escalation.- Production Kubernetes lifecycle experience: building clusters, upgrading them and recovering them when they break. RKE2, K3s or another CNCF-certified distribution is preferred over managed-only experience.- Helm proficiency beyond installing public charts: values management, chart versioning, multi-chart upgrade and rollback, and debugging failed releases.- Deep hands-on experience with at least one major public cloud and working knowledge of a second, covering networking, identity and cost management.- Terraform and Ansible at production scale, as reusable and reviewed code rather than one-off scripts.- Networking fundamentals: routing, NAT, firewalling, DNS and TLS termination, plus the ability to debug a hybrid connectivity problem end to end.- Working knowledge of OIDC authentication and how identity providers integrate with Kubernetes and platform applications.- Practical experience running a Prometheus and Grafana monitoring stack and a centralised log pipeline.- Scripting in Python and Bash, and comfort with YAML-heavy configuration.- Strong ownership and automation instinct, clear written communication for runbooks and incident reports, and availability for a shared on-call https://jobeax.com/link/kkVMsEJ7kyGJ0poP Requirements :- Experience operating AI or HPC clusters, including accelerator-aware scheduling and node health management.- Exposure to non-GPU AI accelerators and their distinct driver, runtime and scheduling models.- Experience deploying a vendor-supplied platform product into a customer or partner environment, including handover and upgrade cycles.- Policy-as-code tooling such as OPA, Kyverno or Sentinel, and experience with air-gapped or restricted-egress deployments. (ref:hirist.tech)
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