AI Operations Engineer (R-19381)

Responsibilities

• Build and maintain observability standards (logs, metrics, traces, events) and dashboards using Splunk Observability.
• Configure and tune alerts and SLOs to reduce noise and improve signal quality.
• Embed observability and reliability checks into CI/CD pipelines.
• Analyze telemetry to detect anomalies and support faster incident triage and root-cause analysis.
• Implement automated runbooks and remediation workflows using scripts and tooling.
• Operate and optimize telemetry pipelines with a focus on data quality, scale, and cost efficiency.
• Support monitoring of AI/LLM services for latency, errors, and cost anomalies where applicable.
• Own and evolve observability platforms, standards, dashboards, alerting strategies, and SLOs.

Essential skills and/or Certifications

• Bachelor’s degree in Computer Science, Artificial Intelligence or related field
• Hands-on experience with observability and monitoring tools (e.g., Splunk).
• Experience working in cloud-native environments (GCP preferred).
• Experience with CI/CD pipelines and automation (Python, Bash, or similar).
• Solid understanding of production incidents and operational workflows.
• Deep experience with automation, event correlation, and auto-remediation
• Proficiency in Microsoft Office Suites Skills
• Show an ownership mindset in everything you do; be a problem solver, be curious and be inspired to take action, be proactive, seek ways to collaborate and connect with people and teams in support of driving success.
• Continuous growth mindset, keep learning through social experiences and relationships with stakeholders, experts, colleagues and mentors as well as widen and broaden your competencies through structural courses and programs.
• Where applicable, fluency in English and languages relevant to the working market.