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Intetics

1143 | Senior DevOps Engineer

LocationNew Zealand
Work moderemote
Typefull-time
DepartmentDevOps
Company size1,001–5,000 people
First seen1w ago
Last seen1d ago
About the company
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.
Benefits
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
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8+ years of experience in Site Reliability Engineering, DevOps, Cloud Engineering, Infrastructure Engineering, or related field.
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Strong hands-on experience with cloud platforms, including AWS, GCP, Azure, and/or OpenShift (OCP).
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Deep experience with Kubernetes, containers, and production container orchestration.
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Experience building and maintaining highly available, scalable, and secure production infrastructure.
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Strong experience with Infrastructure as Code, preferably Terraform, and configuration/automation tools such as Ansible.
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Strong scripting and automation skills using Python, Bash, or similar languages.
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Strong understanding of Linux, networking, security, cloud architecture, and distributed systems.
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Strong experience with observability and monitoring platforms such as Prometheus, Grafana, Loki, CloudWatch, or equivalent tools.
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Experience with incident response, root-cause analysis, production troubleshooting, and reliability engineering practices.
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Experience using AI-assisted engineering tools to improve infrastructure automation, troubleshooting, documentation, code generation, or operational workflows.
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Ability to identify opportunities where AI and automation can reduce operational toil, improve signal detection, and accelerate incident investigation.
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Experience building and maintaining CI/CD pipelines using GitHub, GitLab, Bitbucket, or similar platforms.
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Ability to provide technical leadership, mentor engineers, and help drive a culture of automation, reliability, and continuous improvement.
Minimal Requirements
Nice to have
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Minimum 8 years of experience in SRE, DevOps, Cloud Engineering, Infrastructure Engineering, or a related field.
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Strong hands-on experience with cloud providers like AWS, GCP, Azure, and OpenShift (OCP).
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Strong understanding of Linux, networking, security, cloud architecture, and distributed systems.
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Strong experience with Kubernetes, Infrastructure as Code (terraform, tofu, CloudFormation), and automation (Python, bash, PowerShell).
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Proven experience supporting highly available production systems, including observability, incident response, troubleshooting, and reliability improvements.
Preferred Qualifications
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Experience integrating LLMs or AI-enabled tools into engineering or operational workflows.
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Familiarity with AI-assisted log analysis, anomaly detection, incident summarization, or root-cause investigation.
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Experience building internal automation or tooling that combines APIs, scripting, infrastructure data, and AI models.
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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
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Training & Development
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