Minimum qualifications:- Bachelor's degree in Computer Science or IT-related field, or equivalent practical experience.
- 2 years of experience with systems automation.
- 2 years of experience with technical infrastructure (e.g., deployment, maintenance, troubleshooting).
Preferred qualifications:- 3 years of experience with Production IT experience with evidence of system administration or Enterprise on-prem or cloud services.
- Experience in at least two of the following areas: Operating Systems, Networking, Scripting and Automation.
- Knowledge of Windows and Linux operating systems.
- Exposure to Machine Learning Operations (MLOps) practices, such as staging telemetry data, tracking anomaly detection models, or configuring performance monitoring dashboards.
About the jobYou will be joining the Fleet Infrastructure Engineering (FIE) team to build and manage key infrastructure systems. We power global operations for Alphabet, from physical security and badge access control to IoT monitoring and data center operational technology. We are currently moving beyond manual, reactive alerting toward proactive, AI-driven, self-healing ecosystems. You will apply systems engineering to solve issues from smart surveillance to advanced radio systems.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $116000 - $166000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities- Provide technical expertise to support projects, with the goal of improving efficiency, scalability, and reducing manual effort.
- Leverage coding, scripting, and systems engineering skills to automate system administration tasks and eliminate repetitive work. Review code developed by other developers and provide feedback to ensure best practice.
- Implement automated self-healing workflows by developing and deploying configuration orchestration agents that execute remediations based on automated ML recommendations.
- Track fleet health and anomaly feature attributions via operational dashboards to assist in-depth engineering debugging during critical service outages.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
