Senior DevOps Engineer

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 fieldStrong hands-on experience with cloud platforms, including AWS, GCP, Azure, and/or OpenShift (OCP)Deep experience with Kubernetes, containers, and production container orchestrationExperience building and maintaining highly available, scalable, and secure production infrastructureStrong experience with Infrastructure as Code, preferably Terraform, and configuration/automation tools such as AnsibleStrong scripting and automation skills using Python, Bash, or similar languagesStrong understanding of Linux, networking, security, cloud architecture, and distributed systemsStrong experience with observability and monitoring platforms such as Prometheus, Grafana, Loki, CloudWatch, or equivalent toolsExperience with incident response, root-cause analysis, production troubleshooting, and reliability engineering practicesExperience using AI-assisted engineering tools to improve infrastructure automation, troubleshooting, documentation, code generation, or operational workflowsAbility to identify opportunities where AI and automation can reduce operational toil, improve signal detection, and accelerate incident investigationExperience building and maintaining CI/CD pipelines using GitHub, GitLab, Bitbucket, or similar platformsAbility 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 fieldStrong hands-on experience with cloud providers like AWS, GCP, Azure, and OpenShift (OCP)Strong understanding of Linux, networking, security, cloud architecture, and distributed systemsStrong 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 workflowsFamiliarity with AI-assisted log analysis, anomaly detection, incident summarization, or root-cause investigationExperience building internal automation or tooling that combines APIs, scripting, infrastructure data, and AI modelsUnderstanding of how to use AI safely in production engineering environments, including data security, access controls, validation, and human review Benefits Paid Time OffWork From HomeTraining & Development