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.