# 1143 | Senior DevOps Engineer

**Company:** [Intetics](null/companies/gDvZSRWtedxcHZawvZWQAz.md)
**Location:** Remote
**Workplace:** remote
**Department:** DevOps

[Apply for this job](null/view/388fe64d-274d-461e-a8d7-455def117405)

## Description

Intetics Inc., a leading American technology company specializing in custom software application development, distributed professional teams creation, software product quality assessment, and “all-things-digital” solutions, is on the lookout for a **Senior DevOps Engineer** to join our team and provide exceptional customer support.

**About the project:**

The project is a cybersecurity platform that consolidates vulnerability, threat, and asset data to help organizations identify, prioritize, and remediate critical security exposures. It integrates data from a wide range of security tools and provides centralized capabilities for risk-based vulnerability management, workflow automation, and scalable exposure management.

Working hours are in UTC+10 / UTC+11.

**What You Will Do**

**Maintain Reliable, Secure, and AI-Assisted Production Operations**  
Keep production systems highly available, secure, patched, and performant. Use AI-assisted tooling to accelerate troubleshooting, identify risks, analyze incidents, and improve operational response.

**Build and Maintain Kubernetes, Cloud, and DevOps Infrastructure**  
Own and improve Kubernetes clusters, containerized workloads, Infrastructure as Code, CI/CD pipelines, and cloud infrastructure. Leverage AI-assisted development and automation tools to improve delivery speed, configuration quality, and operational consistency.

**Build Observability and Automation That Reduces Toil**  
Improve monitoring, alerting, logging, dashboards, and automated remediation to identify issues earlier and reduce repetitive operational work. Apply AI and intelligent automation to correlate signals, surface anomalies, assist with root-cause analysis, and automate common SRE workflows.

## Requirements

-   8+ years of experience in Site Reliability Engineering, DevOps, Cloud Engineering, Infrastructure Engineering, or related field.
-   Strong hands-on experience with cloud platforms, including AWS, GCP, Azure, and/or OpenShift (OCP).
-   Deep experience with Kubernetes, containers, and production container orchestration.
-   Experience building and maintaining highly available, scalable, and secure production infrastructure.
-   Strong experience with Infrastructure as Code, preferably Terraform, and configuration/automation tools such as Ansible.
-   Strong scripting and automation skills using Python, Bash, or similar languages.
-   Strong understanding of Linux, networking, security, cloud architecture, and distributed systems.
-   Strong experience with observability and monitoring platforms such as Prometheus, Grafana, Loki, CloudWatch, or equivalent tools.
-   Experience with incident response, root-cause analysis, production troubleshooting, and reliability engineering practices.
-   Experience using AI-assisted engineering tools to improve infrastructure automation, troubleshooting, documentation, code generation, or operational workflows.
-   Ability to identify opportunities where AI and automation can reduce operational toil, improve signal detection, and accelerate incident investigation.
-   Experience building and maintaining CI/CD pipelines using GitHub, GitLab, Bitbucket, or similar platforms.
-   Ability to provide technical leadership, mentor engineers, and help drive a culture of automation, reliability, and continuous improvement.

**Minimal Requirements**

-   Minimum 8 years of experience in SRE, DevOps, Cloud Engineering, Infrastructure Engineering, or a related field.
-   Strong hands-on experience with cloud providers like AWS, GCP, Azure, and OpenShift (OCP).
-   Strong understanding of Linux, networking, security, cloud architecture, and distributed systems.
-   Strong experience with Kubernetes, Infrastructure as Code (terraform, tofu, CloudFormation), and automation (Python, bash, PowerShell).
-   Proven experience supporting highly available production systems, including observability, incident response, troubleshooting, and reliability improvements.

**Preferred Qualifications**

-   Experience integrating LLMs or AI-enabled tools into engineering or operational workflows.
-   Familiarity with AI-assisted log analysis, anomaly detection, incident summarization, or root-cause investigation.
-   Experience building internal automation or tooling that combines APIs, scripting, infrastructure data, and AI models.
-   Understanding of how to use AI safely in production engineering environments, including data security, access controls, validation, and human review.

## Benefits

-   Paid Time Off
-   Work From Home
-   Training & Development