AI Ops Engineer

About the role

The AI Ops Engineer will build the operational backbone for enterprise AI platforms, ensuring safe, repeatable, and scalable AI product releases. This role focuses on release discipline, LLMOps, automation, governance, cost visibility, and self‑service standards for AI‑native delivery teams.

Key responsibilities

• Design and maintain CI/CD pipelines and promotion controls for AI applications, agents, platform services, and configuration changes across environments.
• Embed LLMOps and lifecycle management controls to make model releases auditable and compliant.
• Implement progressive delivery patterns such as canary releases, controlled rollouts, and rollback readiness to reduce production risk.
• Integrate AI quality checks, telemetry, dashboards, runbooks, and readiness criteria into the delivery lifecycle.
• Provide AI FinOps visibility and governance for model usage, token spend, platform capacity, and quota management.
• Create reusable templates, standards, onboarding guidance, and operational playbooks for self‑service AI delivery.

Required profile

• Strong production engineering background operating cloud‑native, AI, or high‑scale API platforms in an enterprise environment.
• Hands‑on experience with CI/CD tools such as GitHub Actions and environment management.
• Working knowledge of LLMOps, telemetry, release governance, and production‑readiness practices.
• Experience with observability, change control, service reliability, and continuous operational improvement.
• Ability to partner with platform engineering, QA/SRE, cybersecurity, architecture, product, and delivery teams.

Required skills

• CI/CD
• GitHub Actions (or equivalent automation)
• LLMOps
• Telemetry and observability
• Cloud‑native platform engineering
• AI lifecycle management