AI Ops Engineer - Hybrid, Observability & Automation

Zerto; San Juan (PR); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Hewlett Packard Enterprise in Puerto Rico seeks an AI Ops Engineer to design intelligent monitoring, build data pipelines, and deploy predictive models for incident reduction. This hybrid role requires regular in-office collaboration.

You will collaborate with DevOps, SRE, and security teams, bring expertise in Python and observability tools, and contribute to reliable, scalable platforms across cloud and on‑premises environments.

Qualifications

• Bachelor's degree in CS, IT, or a related field.
• 5–8 years in IT operations, reliability or platform engineering.
• Experience with AI-driven operations and automation is essential.

Responsibilities

• Design and maintain intelligent monitoring and incident response workflows across both cloud and on-premises systems.
• Build and optimize data pipelines to collect, normalize, and enrich logs, metrics, and events for operational analytics.
• Develop and deploy predictive models to detect anomalies, forecast outages, and reduce mean time to resolution.
• Automate remediation runbooks and integrate alerting with service management platforms to enhance system reliability.
• Collaborate with DevOps, Site Reliability, and security teams to improve platform performance, availability, and operational governance.

Skills

Python automation

Cloud platform operations

ML lifecycle knowledge

ITSM familiarity

Education

Bachelor’s degree in Computer Science, Information Technology, or a related field

Tools

Splunk

Datadog

Prometheus

Grafana

ServiceNow

PagerDuty

Rundeck

Job description

Hewlett Packard Enterprise in Puerto Rico seeks an AI Ops Engineer to design intelligent monitoring, build data pipelines, and deploy predictive models for incident reduction. This hybrid role requires regular in-office collaboration.

You will collaborate with DevOps, SRE, and security teams, bring expertise in Python and observability tools, and contribute to reliable, scalable platforms across cloud and on‑premises environments.