Job responsibilities

• Uses enterprise-authorized AI capabilities within the work environment to accelerate infrastructure analysis and design documentation, validating outputs and handling operational data according to sensitivity and security requirements. 
• Applies reuse-first, AI-assisted practices within delivery and automation routines to identify recurring issues and validate remediation options, ensuring changes are traceable/auditable and aligned to resiliency and security expectations.
• Own and continuously improve SLOs/SLIs, error budgets, on-call readiness, and operational excellence for storage services.
• Lead incident response for storage outages/performance degradations; drive RCAs and implement preventative actions.
• Create and maintain runbooks, escalation paths, and standardized operational procedures.
• Operate and enhance block/file/object storage platforms across on-prem and/or cloud environments.
• Perform performance tuning, capacity planning, lifecycle management, and resiliency testing (failover/DR validation).
• Partner with infrastructure, network, OS, database, and application teams to meet workload requirements and reliability targets.
• Build automation for provisioning, patching, upgrades, replication, backup/restore, and compliance checks.
• Implement AI-driven observability/AIOps (telemetry correlation, anomaly/regression detection, LLM-assisted incident/runbook workflows) with accuracy, auditability, and safe rollout.

Required qualifications, capabilities, and skills

• Formal training or certification on infrastructure engineering concepts and 5+ years applied experience
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.
• Strong knowledge of storage fundamentals (RAID/erasure coding, replication, snapshots, tiering/caching, IOPS/latency, multipathing, SAN/NAS, object semantics).
• Hands-on experience with at least one major storage ecosystem (e.g., NetApp, Dell EMC PowerStore/Isilon, Pure, Hitachi, Ceph, IBM, or cloud storage services).
• Solid Linux fundamentals, including system performance, networking basics, and kernel/storage-stack concepts.
• Strong scripting/programming in one or more of Python, Go, Bash.
• Experience with observability stacks (e.g., Prometheus/Grafana, ELK/OpenSearch, Splunk, Datadog, OpenTelemetry).
• Proven incident management skills and ability to operate effectively in an on-call rotation.
• Practical AI/data skills for operations (anomaly detection/forecasting/correlation/classification; feature extraction and evaluation; integrating AI into production tooling/CI/CD; safe LLM use with guardrails and human-in-the-loop review).

• Kubernetes storage (CSI), stateful workloads, and container platform operations.
• Infrastructure as Code (Terraform/CloudFormation) and configuration management (Ansible/Chef/Puppet).
• Streaming/queue tooling for telemetry and event pipelines (e.g., Kafka).
• Experience with ITSM/event management platforms (e.g., ServiceNow).
• Backup/DR products and strategy design, including RPO/RTO tradeoffs.
• Security controls for data platforms (KMS/HSM, secrets management, key rotation).
• Experience building/operating controlled self-service platforms with guardrails to reduce toil at scale.