Staff AI Solutions Deployment Developer, Applied AI
$183k - $265k per yearUrban Ridge Supplies
This posting is for an existing vacancy.
Google utilizes AI tools to assist in assessing candidates in our hiring processes. In most instances, this position requires in-person interviews as part of the hiring process. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Waterloo, ON, Canada; New York, NY, USA . Minimum qualifications:- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 8 years of experience in customer-facing technical roles (e.g., Solutions Architect, Forward Deployed Specialist, Principal Consultant) with a focus on enterprise systems.
- 3 years of experience writing code in one or more programming languages (e.g., Python, Java).
- Experience with Large Language Models (LLMs), prompt development, and conversational AI frameworks.
- Master's degree in Engineering, Computer Science, or related technical fields.
- Experience building and scaling complex production systems and working with multi-stakeholder technical projects, and with Google Cloud Platform (GCP) environment setup and cloud architecture.
- Experience with telecom customer deployments, and other similar industries.
- Experience in technical consulting, navigating the governance, security, and procurement complexities.
- Ability to bridge the gap between LLM capabilities and deterministic enterprise systems (e.g., Salesforce, SAP).
- Ability to lead cross-functional teams and influence executive stakeholders in large, cross-functional organizations.
- Contribute to technical engagements from 0-to-1, defining the agent’s brain (reasoning paths), hands (API tools), and guardrails to ensure resilient, autonomous infrastructure.
- Mitigate high-stakes customer escalations, providing direct debugging and architectural guidance to customer developers and C-suite teams.
- Deploy pre-general availability features in real-world environments to stress-test capabilities, synthesizing field intelligence to directly influence the Google Cloud Product and Developer roadmaps.
- Advocate for foundational architectural frameworks such as Agent-MVC and pioneer advanced techniques like cyclic reasoning loops to accelerate time-to-value.
- Bridge the gap between non-deterministic LLM outputs and deterministic enterprise systems (e.g., SAP, Salesforce) to ensure safe, reliable agentic operations within guardrails.
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