MLOPS Engineer

Conch Technologies Inc; Dallas (TX); On-site; USD 140,000 - 180,000; Full time

Job summary

Conch Technologies, Inc. is seeking an experienced MLOPS Engineer to lead infrastructure for Generative and Predictive AI platforms. The role focuses on OpenShift-based GPU containers, scalable SuperPod architectures, and end-to-end platform observability.

You will drive production readiness, incident analysis, and automation leveraging Grafana and Splunk across a distributed environment. Onsite in preferred locations with 12-month contract expectations.

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.

Responsibilities

• 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.

Skills

Infrastructure engineering

CI/CD pipelines

Linux

Kubernetes

AI/ML platforms

Tools

OpenShift

Grafana

Splunk

Job description

Title: MLOPS Engineer

Location Preferences: Charlotte, NC | Texas | Arizona | New Jersey (Onsite)

Duration: 12 Months Contract

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.
• - Operate in regulated enterprise environments with strong risk and control focus.