MLops Engineer

Project overview

Role: MLOPS Engineer

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

Please share suitable MLOPS Engineer profiles who are comfortable with the above locations and rate. 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

Required Qualifications

Desired Qualifications

Job Expectations

Job Facts: Contract Corp To Corp | Contract W2 | Contract Independent | 12 Months | On-site | $50 - $60/hr

Skills: mlops, ai, azure, gpu

Reference: Dice Id: 10114281 | Position Id: 9124586

• 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.
• 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.
• 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 Google Cloud Platform 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.
• 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.

Project details

• Start: flexible
• Duration: 12 Months
• Remote: onsite
• Location: Dallas, TX, US, US