MLOps Engineer

Title: MLOps EngineerLocation: Charlotte, NC (Onsite) or remote for people living in Texas, Arizona, New JerseyKey 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 ExpectationsParticipate 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.