| Location | Philadelphia, PA |
DevOps / Kubernetes Operations Engineer (Contract)
Job at a Glance:
3-month contract
Remote
Pay: $55 per hour + optional medical, dental, vision, and 401(k)
Position Summary
Our client is seeking a DevOps / Kubernetes Operations Engineer to support a short-term, high-impact initiative within an AI-focused platform team. This team is responsible for enabling access to frontier AI models, open-weight models, and deploying GPU-based infrastructure on Kubernetes. The role is heavily focused on operations, platform reliability, observability, and Kubernetes administration, with little to no software development required.
This is a fast-moving engagement expected to last up to 12 weeks, with no anticipated extension beyond the project timeline. Candidates located in CST or EST time zones are preferred.
Responsibilities
Configure, manage, and optimize Kubernetes environments.
Support deployment and operational readiness of services running on Kubernetes.
Build and maintain observability solutions and monitoring dashboards using Grafana.
Improve platform reliability, monitoring, alerting, and operational visibility.
Administer and troubleshoot Kubernetes clusters and related infrastructure.
Support Helm deployments and configuration management.
Assist with CI/CD processes and operational automation.
Partner with AI platform teams supporting GPU-based infrastructure and model-serving environments.
Monitor system health, performance, and availability while resolving operational issues.
Required Qualifications
Strong hands-on experience with Kubernetes administration and configuration.
Experience with Grafana, monitoring, observability, and dashboard development.
Background in DevOps, Site Reliability Engineering (SRE), or Infrastructure Operations.
Experience deploying and supporting services within Kubernetes environments.
Knowledge of Helm, CI/CD pipelines, and containerized infrastructure.
Strong troubleshooting skills across systems, infrastructure, and platform operations.
Experience working in Linux-based environments.
Preferred Qualifications
Experience with AWS cloud services.
Exposure to GPU-enabled infrastructure or AI/ML platform environments.
Familiarity with Python or other scripting languages.
Experience supporting AI, machine learning, or model-serving platforms.
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