AI Operations Engineer

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.

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.

See also