Site Reliability Engineer - Saudi Only

About Lucidya Lucidya is an AI-native platform for customer experience (CX) intelligence that manages entire customer lifecycles autonomously, from initial engagement through retention and growth. Unlike platforms that only surface insights and leave the action to you, Lucidya closes the loop with proprietary NLU technology built in-house and trained on millions of multilingual conversations. This enables marketing, support, CX, and research teams to deliver personalized experiences that drive measurable improvements in customer satisfaction, retention, and lifetime value. As we continue scaling globally, the reliability, performance, and resilience of our infrastructure become mission-critical to everything we do. Why this role matters At Lucidya, our platform processes massive volumes of real-time customer data. Any downtime, latency, or instability directly impacts our customers’ ability to make decisions and serve their own users. This role exists to make sure that doesn’t happen. As a Site Reliability Engineer, you’ll sit at the heart of our platform’s stability, owning the reliability of our cloud infrastructure and ensuring it scales seamlessly as we grow. You won’t just react to issues; you’ll anticipate them, design systems that prevent them, and build automation that removes them entirely. If you enjoy solving complex infrastructure challenges, eliminating inefficiencies, and building systems that “just work” - this is where you’ll thrive. What You’ll Do You’ll be responsible for outcomes, not just tasks. Here’s what success looks like in this role: You’ll make reliability the default - You’ll design and maintain infrastructure that is highly available, fault-tolerant, and scalable - You’ll proactively identify and eliminate single points of failure before they become incidents - You’ll ensure our production systems remain stable, even under increasing scale and load You’ll own and optimize our cloud environments - You’ll manage and continuously improve workloads across AWS, GCP, or Azure - You’ll use Infrastructure as Code (Terraform) to standardize and scale infrastructure - You’ll optimize resource usage to balance performance and cost You’ll run and improve Kubernetes in production - You’ll operate and scale Kubernetes clusters (EKS, GKE, etc.) with confidence - You’ll troubleshoot issues quickly and ensure smooth deployments and upgrades - You’ll ensure our containerized workloads perform reliably at scale You’ll build strong observability and respond to incidents - You’ll implement and refine monitoring systems using tools like Prometheus, Grafana, Datadog, or ELK - You’ll define alerting that is meaningful, not noisy - You’ll respond to incidents, lead root cause analysis, and ensure we learn from every failure You’ll automate everything that shouldn’t be manual - You’ll write scripts and build tooling to eliminate repetitive operational work - You’ll continuously improve infrastructure efficiency through automation - You’ll promote a culture where manual work is a temporary state, not the norm You’ll collaborate to improve the entire system - You’ll work closely with DevOps and engineering teams to solve performance bottlenecks - You’ll contribute to CI/CD improvements and deployment reliability - You’ll help shape reliability best practices across the organization What success looks like (First 90 Days) First 30 days: - You’ve built a strong understanding of our infrastructure, systems, and workflows - You’re contributing to day-to-day operations with support from the team - You’ve started identifying areas for improvement in automation and reliability By 90 days: - You’re independently managing infrastructure tasks and troubleshooting issues - You’re actively contributing to reliability and scalability improvements - You’ve taken ownership of parts of our infrastructure and are improving them Requirements Who You Are This is what will make you successful in this role: - You’ve spent ~3 years working in SRE, DevOps, or infrastructure engineering, and you’ve seen what breaks at scale - You’re comfortable working in cloud environments like AWS, GCP, or Azure—and you understand how distributed systems behave - You’ve worked hands-on with Kubernetes in production and know how to troubleshoot it when things go wrong - You don’t just fix issues - you ask why they happened and make sure they don’t happen again Technically, you likely: - Use Terraform (or similar IaC tools) to manage infrastructure - Work confidently with Docker and Kubernetes - Write scripts in Python, Bash, or similar to automate workflows - Understand CI/CD pipelines (Jenkins, GitHub Actions, Bitbucket, etc.) - Have a solid grasp of networking, load balancing, and high-availability design When it comes to monitoring: - You’ve implemented tools like Prometheus, Grafana, Datadog, or ELK - You know the difference between useful alerts and noise - You focus on signals that actually drive action What sets you apart: - You take owne

You’ve spent ~3 years working in SRE, DevOps, or infrastructure engineering, and you’ve seen what breaks at scale. You’re comfortable working in cloud environments like AWS, GCP, or Azure—and you understand how distributed systems behave. You’ve worked hands-on with Kubernetes in production and know how to troubleshoot it when things go wrong. You don’t just fix issues - you ask why they happened and make sure they don’t happen again. Technically, you likely: Use Terraform (or similar IaC tools) to manage infrastructure. Work confidently with Docker and Kubernetes. Write scripts in Python, Bash, or similar to automate workflows. Understand CI/CD pipelines (Jenkins, GitHub Actions, Bitbucket, etc.). Have a solid grasp of networking, load balancing, and high-availability design. When it comes to monitoring: You’ve implemented tools like Prometheus, Grafana, Datadog, or ELK. You know the difference between useful alerts and noise. You focus on signals that actually drive action.

You’ll design and maintain infrastructure that is highly available, fault-tolerant, and scalable. Proactively identify and eliminate single points of failure before they become incidents. Ensure production systems remain stable, even under increasing scale and load. Manage and continuously improve workloads across AWS, GCP, or Azure. Use Infrastructure as Code (Terraform) to standardize and scale infrastructure. Optimize resource usage to balance performance and cost. Operate and scale Kubernetes clusters (EKS, GKE, etc.) with confidence. Troubleshoot issues quickly and ensure smooth deployments and upgrades. Ensure containerized workloads perform reliably at scale. Implement and refine monitoring systems using tools like Prometheus, Grafana, Datadog, or ELK. Define alerting that is meaningful, not noisy. Respond to incidents, lead root cause analysis, and ensure we learn from every failure. Write scripts and build tooling to eliminate repetitive operational work. Continuously improve infrastructure efficiency through automation. Promote a culture where manual work is a temporary state, not the norm. Work closely with DevOps and engineering teams to solve performance bottlenecks. Contribute to CI/CD improvements and deployment reliability. Help shape reliability best practices across the organization.