Senior SaaS Operations Engineer

Fastly•Quinte West, ON

10d•$132,060 - $186,444•Hybrid

About The Position

Fastly is hiring a Senior SaaS Operations Engineer to support its in-office and remote employees and keep IT infrastructure effective, secure, and up to date. The role supports operations across global offices in a fast-paced, high-growth environment and works as part of a distributed team. It calls for experience spanning systems administration and advanced service desk support, attention to detail, process improvement, and the ability to scale a large SaaS environment. The engineer will also help Fastly adopt AI tools safely and effectively. The position leads the design, implementation, and maintenance of information systems, promotes their availability, security, and performance, and provides expert troubleshooting for critical issues. It includes administering enterprise AI platforms, coordinating with Security and Legal on integrations and governance, and building AI-assisted automations. The role also involves mentoring engineers, working cross-functionally, researching technology, and documenting systems, processes, and AI usage guidance. The role requires availability during core business hours. It is open to candidates in San Francisco, New York, Denver, or remotely in the United States. Fastly describes its general work model as largely hybrid, allowing employees to split their time between office and home. The estimated salary range is $132,060 to $186,444. The role may be eligible for equity and a discretionary bonus. Fastly offers medical, dental, and vision insurance; family planning and mental health support; an Employee Assistance Program; life, disability, and accident insurance; flexible vacation; accrued paid sick leave; a 401(k) with company match; an Employee Stock Purchase Program; paid local holidays; and paid company wellness days.

Requirements

10 or more years of experience in IT support roles, including 5 or more years in a senior or lead engineering position.
Deep understanding of system architecture, design patterns, and best practices.
Proven ability to lead and mentor engineering teams.
Strong experience with cloud-based IT environments such as Google Workspace, Zoom, Slack, GitHub, and Atlassian, with advanced domain knowledge of Okta.
Hands-on experience deploying or administering enterprise AI tools such as Claude, OpenAI, or Gemini, including identity integration and data-handling controls.
Experience with automation and scripting for system administration.
Excellent communication skills and the ability to explain technical information to technical and non-technical audiences.
Ability to align technology with business objectives and make data-driven recommendations.
Availability during core business hours.

Nice To Haves

Infrastructure-as-code experience, such as Terraform or Ansible.
Experience building with Claude, OpenAI, or Gemini APIs, including agents, MCP servers, or prompt engineering for business workflows.
Experience establishing AI governance practices, such as connector reviews or acceptable-use policies.
Relevant certifications, such as Okta Certified Professional or Google Cloud Certified Professional Cloud Architect.

Responsibilities

Lead the design, implementation, and maintenance of complex information systems to support high availability, security, and performance.
Proactively identify and mitigate system vulnerabilities and performance bottlenecks.
Lead and mentor engineers by providing technical guidance and support.
Collaborate with cross-functional teams, including product and security, to deliver solutions aligned with business objectives.
Administer enterprise AI platforms, including Claude, OpenAI, and Gemini, with responsibility for SSO/SCIM, access controls, and license management.
Partner with Security and Legal to review AI connectors and integrations using least-privilege and data-governance principles.
Build AI-assisted automations to reduce manual IT work and improve the employee experience.
Research and recommend new technologies, including emerging AI tools.
Maintain documentation for systems and processes, including AI usage guidelines.
Provide expert-level troubleshooting for critical system issues and help shape engineering best practices.