Cloud Engineer

Flexton Inc.

Austin (TX)

Hybrid

USD 68,880 - 82,656

Full time

14 days+

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Job summary

A leading IT services company is seeking a Sr. Cloud & Kubernetes Engineer in Austin, Texas. This role involves designing and implementing AI workflows using Agentic AI principles, deploying and managing Kubernetes clusters, and implementing cloud-native solutions. Ideal candidates have at least 5 years of relevant experience and strong skills in Kubernetes and DevOps practices. This position offers a contract employment type with remote working options.

Qualifications

Minimum 5 years of experience in Cloud Operations, DevOps, or Kubernetes-based environments.
Hands-on experience with Kubernetes fundamentals and architecture.
Strong Linux administration skills and proficiency in scripting languages.
Excellent communication skills for technical discussions.

Responsibilities

Develop and manage AI learning workflows using Agentic AI principles.
Deploy, monitor, and troubleshoot Kubernetes clusters.
Implement cloud-native solutions for microservices and CI/CD pipelines.
Configure monitoring and logging tools for high availability.

Skills

Cloud Operations

Kubernetes

DevOps

Linux administration

Scripting languages (Bash, Python)

Monitoring tools (Prometheus, Grafana, ELK)

Incident management (Jira, ServiceNow)

Communication skills

Tools

Kubernetes

Prometheus

Grafana

ELK stack

Jira

ServiceNow

Job description

This range is provided by Flexton Inc.. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$50.00/hr - $60.00/hr

Direct message the job poster from Flexton Inc.

One of my clients is looking for Sr. Cloud & Kubernetes Engineer::W2@ Austin TX (Remote) please share me your updated resume and desire rate for this position.

Job Summary

We are seeking an experienced Cloud and Kubernetes Engineer to design, implement, and manage workflows that enable AI systems to learn and operate effectively using Agentic AI methodologies. The ideal candidate will have strong expertise in Kubernetes, cloud-native architectures, and DevOps practices, with the ability to translate complex technical concepts into scalable AI workflows. Experience with LangGraph or LangChain is a plus. Rag models.

Key Responsibilities

Develop and manage AI learning workflows using Agentic AI principles, integrating with platforms such as LangGraph and LangChain.
Deploy, monitor, and troubleshoot Kubernetes clusters, including Pods, Deployments, Services, ConfigMaps, Secrets, API server, and Etcd.
Implement cloud-native solutions, supporting microservices, containerization, and CI/CD pipelines.
Configure and manage monitoring and logging tools (Prometheus, Grafana, ELK, or equivalent) to ensure high availability and observability.
Collaborate with cross-functional teams to design automated incident management and ticketing workflows using tools like Jira or ServiceNow.
Apply networking knowledge for DNS, Load Balancing, and Ingress to optimize system performance.
Maintain high standards of accountability and ownership, driving issues to resolution while communicating effectively with both technical and non-technical stakeholders.

Required Skills & Experience

Minimum 5 years of experience in Cloud Operations, DevOps, or Kubernetes-based environments.
Hands-on experience with Kubernetes fundamentals, architecture, controllers, admission webhooks, and basic troubleshooting.
Strong Linux administration skills and proficiency in scripting languages (Bash, Python preferred).
Basic understanding of cloud-native concepts, microservices, and CI/CD pipelines.
Experience with monitoring/logging tools (Prometheus, Grafana, ELK, or equivalent).
Familiarity with incident management and ticketing workflows (e.g., Jira, ServiceNow).
Excellent communication skills, capable of explaining technical issues to non-technical audiences.
Kubernetes certification (CKA/CKS) or equivalent practical experience.

Preferred Skills