Mlops Engineer

Role : MLOPS Engineer

Location Preferences: Charlotte, NC | Texas | Arizona | New Jersey

Experience: 10+ Years

Candidates with strong experience in MLOps, CI/CD pipelines, cloud platforms, model deployment, monitoring, and automation are preferred.

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

- 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.

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.