Senior DevOps Engineer

🔥 1 hour ago

🇸🇦 Saudi Arabia – Remote

⏰ Full Time

🟠 Senior

⛑ DevOps & Site Reliability Engineer (SRE)

👻 Ghost score 11%

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Lucidya | لوسيديا

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

📣 Marketing

🤖 Artificial Intelligence

💰 $6M Series B on 2022-01

Consulting • Marketing • Artificial Intelligence

Lucidya is a company specializing in social media and online platform analytics. They provide tools and insights that help businesses understand customer sentiment, track brand reputation, and make data-driven decisions. Lucidya's solutions are tailored for engaging with the Arabic-speaking market, leveraging AI technology to process and analyze data for optimal marketing strategies.

📋 Description

• Build, maintain, and improve CI/CD pipelines for reliable and efficient software delivery • Deploy and manage containerized applications using Docker and Kubernetes • Support cloud infrastructure management and automation using Infrastructure as Code tools such as Terraform • Implement and maintain monitoring, logging, and alerting solutions using tools such as Grafana • Automate repetitive operational tasks and support development teams with infrastructure and deployment needs • Troubleshoot deployment, application, and infrastructure issues, identify root causes, and implement solutions • Collaborate with engineers to improve system performance, reliability, and release processes • Follow security best practices across deployment pipelines, applications, and infrastructure • Participate in incident investigation and resolution, escalating complex issues when necessary • Contribute to documentation and continuous improvement of infrastructure workflows and operational processes • Deploy applications and support routine operations within Kubernetes environments • Build, maintain, and troubleshoot CI/CD pipelines for new and existing services • Monitor system health, investigate alerts, and resolve common deployment and infrastructure issues • Support developers with application deployments, environment configurations, and infrastructure requests • Write and maintain scripts to automate repetitive operational tasks • Assist with infrastructure provisioning and configuration management • Collaborate with senior engineers to investigate complex incidents and improve system reliability • Identify opportunities to improve automation, observability, and operational efficiency • Onboard 2–3 services onto the existing Kubernetes environment with CI/CD pipelines, monitoring, and health checks within the first 90 days

🎯 Requirements

• 5–7 years of hands-on experience in DevOps, Cloud Infrastructure, Platform Engineering, or a related role • Practical experience with CI/CD pipelines, Docker, Kubernetes, and cloud infrastructure • Experience building, maintaining, or improving CI/CD pipelines using GitHub Actions, GitLab CI/CD, Jenkins, or similar • Practical experience with Docker and working knowledge of Kubernetes, including application deployments, configurations, and troubleshooting • Familiarity with Terraform, CloudFormation, or similar Infrastructure as Code tools • Hands-on experience with at least one major cloud provider, such as AWS or GCP • Ability to write basic automation scripts using Bash or Python • Familiarity with monitoring, logging, and alerting tools such as Grafana, Prometheus, or similar • Basic knowledge of Ansible, Puppet, or Chef • Working knowledge of Linux systems, networking fundamentals, DNS, HTTP/HTTPS, and troubleshooting connectivity issues • Experience using Git and collaborative software development workflows • Experience working in a SaaS, product-based, or AI-driven environment • Exposure to production Kubernetes environments and container orchestration • Familiarity with advanced monitoring, observability, and incident management practices • Understanding of cloud security, secrets management, and infrastructure access controls • Experience optimizing deployment workflows and improving infrastructure reliability • Python knowledge is a must • Software background is mandatory • Experience optimizing AI deployment workflows and MLOps practices is a nice-to-have • Experience optimizing GPU usage for AI workloads is a nice-to-have

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