AI Operations Engineer

Job Description — AI Ops Engineer (DevOps/MLOps/AIOps)

Key responsibilities

• CI/CD & deployments:  build/maintain pipelines and deployment processes aligned to Cyber/compliance expectations.
• IaC & implementation packages:  create/maintain Infrastructure-as-Code (e.g., Terraform/Bicep) and provide complete technical specs for approved execution.
• Operate & support production:  monitor dashboards/logs, triage incidents, perform RCAs, maintain runbooks, and drive issue closure.
• Alert hygiene:  reduce alert noise, tune rules/thresholds, and ensure alerts are actionable (severity, ownership, playbooks).
• Enterprise integrations & access issues:  troubleshoot and drive fixes for items such as:
• Power Platform ↔ Jira connector issues (incl. allowlisting/service tags)
• HTTPS connector compliance needs
• Citrix VDI / Netskope / proxy access blocks (e.g., VS Code, external tools)
• Cloud cost management (automation-first):  monitor spend, implement tagging/controls, automate recurring cost reporting (avoid manual spreadsheet-heavy processes).

Working model (important)

• Due to  segregation of duties and centralized governance , you may not have persistent elevated/admin access.
• Some changes (RBAC, network/security rules, proxy allowlists, certain provisioning) must be executed by  C&F Service Desk/Cloud/Network/Security  teams.
• Ownership in this role means:  you diagnose, propose the fix, provide IaC/specs + validation steps, coordinate execution, and verify results .

Requirements

Required qualifications

• 3+ years in  DevOps/SRE/Cloud Ops/MLOps/AIOps  (or equivalent).
• Experience with at least one major cloud ( Azure and/or AWS ) in enterprise environments with restricted permissions.
• Hands-on experience with  CI/CD ,  IaC , and  observability/monitoring  tools.
• Strong troubleshooting skills using logs/metrics/traces; scripting/automation with  Python/PowerShell/Bash .
• Strong communication and responsiveness (timely acknowledgements, proactive follow-through).

Preferred

• Experience supporting AI/ML platforms or API-based services.
• Containers/Kubernetes and/or serverless exposure.
• FinOps experience (cost allocation, anomaly detection, optimization).