Staff AI Solutions Engagement Manager, Applied AI
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Minimum qualifications:
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 7 years of experience in customer-facing technical roles (e.g., Solutions Architect, Forward Deployed Engineer, or Principal Consultant).
- 3 years of experience in system architecture and reading code (e.g., Python).
- Experience with Large Language Models (LLMs), prompt engineering, and conversational AI frameworks.
Preferred qualifications:
- 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 GCP environment setup and cloud architecture.
- Experience in technical consulting, navigating the governance, security, and procurement complexities of Fortune 500 environments.
- 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.
About the job
The Google Cloud Applied AI (AAI) Solutions Consulting Team functions as a high-stakes unit responsible for Google’s most strategic AI deployments. We bridge the gap between experimental AI and enterprise reality, acting as the technical and strategic engine that turns complex business problems into production-ready agentic solutions. This team serves as the primary owner for mission-critical delivery, ensuring the end-to-end implementation of AI agents moves seamlessly from initial promise to full-scale production for global customers. Acting as a technical team multiplier, we do more than just build; we define the core best practices and deployment frameworks that set the standard for the entire Google Cloud ecosystem.
In this role, you contribute to the end-to-end delivery of agentic solutions, working with a group of deployment specialists to implement best practices for delivering at scale and velocity. You are a direct builder, a strategic systems thinker, and you navigate complex technical and business challenges. You'll be working with a group of deployment specialists while simultaneously incubating new products and defining the best practices that will enable the entire Google Cloud ecosystem.
Applied AI builds conversational agents deployed at a large scale that achieve very meaningful results in the real world. Some examples include the customer agent built for large call center environments, to fast food ordering handled by our Food AI agent. The team is transforming how enterprises connect with customers through the power of AI. We also offer unique experiences for team members where you get to work directly with the model builders (Google DeepMind / Vertex), learn and work with brilliant AI leaders, and have access to Global 1000 customers via our existing Google Cloud relationships. The opportunity in this space is tremendous.
The Canada base salary range for this full-time position is CAD 195,000-200,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
Please note that the compensation details listed in Canada role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- 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 engineering 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 Engineering roadmaps.
- Advocate for foundational architectural frameworks—such as Agent-MVC and pioneer advanced techniques like meta-prompting and 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 rigid guardrails.
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