MLOps Engineer

Title: MLOps Engineer Location: Charlotte, NC (Onsite) or remote for people living in Texas, Arizona, New Jersey Key Responsibilities: • Lead complex infrastructure initiatives supporting Generative AI and Predictive AI platforms, from design through production operations. • Serve as 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 that detect performance, capacity and resiliency risks early. • 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 with a focus on operational rigor and root-cause prevention. • Mentor engineers and influence stakeholders across a geographically distributed organization. Required Qualifications: • 5+ years of infrastructure engineering experience. • 5+ years troubleshooting complex end-to-end architectures, including CI/CD pipelines. • 5+ years of Linux systems experience. • 4+ years supporting AI/ML platforms. • 4+ years of Kubernetes / container platform experience, including production support. Desired Qualifications: • Experience with Generative AI and Predictive AI platforms. • Hands-on GPU platform operations, including scheduling, quotas and performance tuning. • Experience with OpenShift in GPU-enabled, multi-tenant environments. • Experience designing or operating GPU SuperPods. • Deep observability experience with 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, including cloud-native services, networking, identity and cost optimization. • Strong monitoring of AI signals such as inference latency and GPU utilization. • Experience with BCP/DR, resiliency and highly available architectures. Job Expectations • Participate in a 24x7 on-call rotation. • Own production stability, platform health and customer outcomes. • Operate in a regulated enterprise environment with a strong risk and control focus.