Infrastructure Engineer

Infrastructure Engineer JOB SUMMARY Locations: Charlotte, NC/ Chandler, AZ/ Dallas, TX and Islin, NJ Key Responsibilities Lead complex infrastructure initiatives supporting Generative AI and Predictive AI platforms from design to production operations. Serve as a technical lead for platforms supporting AI/ML model training, inference, and batch workloads. Design, build, deploy, and operate OpenShift-based container platforms optimized for high-performance GPU workloads. Build, support and operate scalable GPU SuperPod architecture with large multi-node GPU clusters. Own monitoring, alerting, and observability using Grafana, Splunk, and enterprise telemetry tools. Define SLIs/SLOs and build actionable alerts to proactively detect performance, capacity, and resiliency risks. Build AI- and agent-based automation tools for self-healing, scaling, diagnostics, and incident remediation. Apply AIOps techniques to reduce alert fatigue and improve platform reliability. Lead production incident analysis and ensure operational rigor and root-cause prevention. Mentor engineers and influence stakeholders across a geographically distributed organization. Participation in a 24x7 on-call rotation. Ownership for production stability, platform health, and customer outcomes. Operate in regulated enterprise environments with strong risk and control focus. Required Qualifications 5+ years of infrastructure engineering experience. 5+ years troubleshooting complex end-to-end architectures(including CI/CD pipeline). 5+ years Linux systems experience. 4+ years supporting AI/ML platforms. 4+ years of Kubernetes / container platform experience including production support. Preferred Qualifications Experience with Generative AI and Predictive AI platforms. Hands-on GPU platform operations including scheduling, quota, and performance tuning. Experience with OpenShift in GPU-enabled, multi-tenant environments. Experience designing or operating GPU Super Pods. Deep experience with observability using Grafana, Splunk, and custom telemetry pipelines. Experience building AI- or agent-driven automation tooling (AIOps). Hands-on experience supporting AI/ML workloads on GCP and Azure, including GPU-backed services and managed AI infrastructure Experience operating hybrid or multi-cloud AI platforms, with an understanding of cloud-native services, networking, identity, and cost optimization for Generative and Predictive AI Strong monitoring of AI signals such as inference latency and GPU utilization. Experience with BCP/DR, resiliency, and highly available architectures.