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SRE / AI Ops Engineer

Themesoft

 

Toronto, ON, Canada

Posted On: 30+ days ago
Experience: 10+ years
Availability: Hybrid
Openings: 1
Category: AI Ops Engineer
Tenure: No Preference/Any
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Description

You will design, build, and operate intelligent, automated reliability solutions across production environments using AI-driven observability and automation practices.

Responsibilities

  • Implement and optimize monitoring solutions using Dynatrace, Splunk, and Moogsoft, leveraging AI/ML capabilities to detect anomalies, predict incidents, and correlate events.
  • Design and build AI-powered operational workflows that automate incident detection, root cause analysis, remediation actions, and post-incident insights.
  • Configure PagerDuty for intelligent alerting and escalation, while building self-healing automation using Ansible, Python, and GitHub Actions.
  • Apply SRE principles including SLOs, SLIs, error budgets, and chaos testing to improve system reliability and reduce operational toil.
  • Build automated CI/CD pipelines with GitHub Actions that integrate observability signals, AI-driven quality gates, and automated rollback workflows.

Required Skills

  • 10+ years of experience in Site Reliability Engineering or DevOps roles.
  • Hands-on expertise with Dynatrace (including Davis AI), Splunk (ITSI, ML Toolkit), and Moogsoft AIOps.
  • Proficiency in Python scripting for automation, data processing, and tooling development.
  • Experience configuring and managing PagerDuty for incident response and alerting policies.
  • Strong command of Ansible for infrastructure automation and configuration management.
  • Skilled in using Git and GitHub Actions to build and maintain automated pipelines.
  • Deep understanding of distributed systems, cloud infrastructure, and reliability engineering principles.
  • Ability to design automated workflows that use AI insights for event correlation and predictive alerting.

Preferred Skills

  • Experience with Red Hat OpenShift and containerized environments (Kubernetes, Docker).
  • Exposure to LLM-based automation or generative AI for operational workflows.
  • Background in building or integrating with ChatOps frameworks and event-driven architectures.

Education

Any Graduate

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