Senior Site Reliability Engineer

Cloudious•Dallas, TX

8d•Onsite

About The Position

The Senior Site Reliability Engineer will enhance system reliability and performance through innovative solutions and best practices. The role leads incident response, automates operational workflows, and partners with development teams to embed reliability principles into the software development lifecycle. It also focuses on AIOps, end-to-end observability, predictive operations, and establishing service objectives and error budgets.

Requirements

The posting states 12+ years of SRE and DevOps experience in the About Position section.
At least 5 years of experience in Site Reliability Engineering, DevOps, or Platform Engineering.
At least 2 years of hands-on experience with AIOps tools and observability platforms.
Experience with LLM-based operations assistants or generative AI for incident management.
Experience with AWS, Azure, or GCP cloud platforms; certifications are a plus.
Proficiency in at least one scripting or programming language: Python, Go, or Java.
Experience with CI/CD tools such as Jenkins, GitHub Actions, ArgoCD, or Tekton.
Familiarity with data pipelines and ML model integration in an operational context.

Nice To Haves

Experience building and maintaining large-scale distributed systems.
Strong analytical and problem-solving skills.
Excellent communication and collaboration abilities.
Experience with container orchestration tools such as Kubernetes and Docker.
Cloud-platform certifications are a plus.

Responsibilities

Lead incident response, post-mortem reviews, and blameless retrospectives to drive continuous improvement.
Automate operational workflows, deployments, and self-healing mechanisms.
Collaborate with development teams to integrate reliability principles early in the software development lifecycle.
Implement and operate AIOps platforms for noise reduction, anomaly detection, and event correlation.
Build end-to-end observability using metrics, logs, traces, and synthetic monitoring tools.
Train machine-learning models or use pre-built AIOps capabilities to predict incidents, detect performance degradation, and identify root causes.
Integrate AIOps tools with ITSM platforms for automated ticket creation and routing.
Establish event correlation and intelligent alerting strategies to reduce alert noise and Mean Time to Recovery.
Enable predictive capacity planning using historical telemetry and forecasting models.
Define and enforce Service Level Objectives, Service Level Indicators, and Error Budgets across critical services and platforms.