MLOps Engineer

Role : MLOps Engineer

Location : Charlotte, NC / Irving, TX / Phoenix, AZ – Onsite from day 1

Duration : Contract

Job description:

In This Role, You Will

• 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, ing, and observability using Grafana, Splunk, and enterprise telemetry tools.
• Define SLIs/SLOs and build actionable s 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 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.

Required Qualifications

• Proven years of infrastructure engineering experience.
• Proven years troubleshooting complex end-to-end architectures(including CI/CD pipeline).
• Proven years Linux systems experience.
• Proven years supporting AI/ML platforms.
• Proven 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, 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.

Job Expectations

• 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.

We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, citizenship status, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law.