Technical Deployment Lead, Google Cloud
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 7 years of experience in product management, systems or solution architecture, software/platform engineering, or technical leadership in customer-facing roles.
- Experience designing end-to-end AI systems (e.g., RAG, vector databases, and API integrations) and defining AI product/workflow specifications.
- Experience leading cross-functional teams, driving technical deployments, and managing stakeholders.
Preferred qualifications:
- Experience in systems design with the ability to explain model architectures, data analysis, feature engineering, and agent design patterns.
- Experience in Process Reimagination, assessing legacy workflows, and designing automated, AI-driven alternatives that enhance operational velocity.
- Experience in context and specification engineering, systematically structuring data, constraints, and operational guidelines to optimize LLM performance.
- Experience in business-aligned evaluation design to define metrics, collect data, and measure a program's effectiveness and impact.
- Experience deploying agents and RAG solutions using vector search, A2A, and MCP for multi-agent collaboration.
- Experience prototyping low-fidelity solutions to validate AI features, user experiences, and model feasibility.
About the job
The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.
As a Technical Deployment Lead (TDL), you will be squarely at the intersection of deep software engineering, cross-functional leadership, and enterprise customer strategy. Working directly with strategic enterprise customers, you will own the overall direction, technical architecture strategy, and ultimate success of high-stake AI deployments. You will define the overall product/process goal, manage infrastructure and data governance constraints upfront, and break down highly complex challenges into actionable roadmaps while collaborating daily across the Delta squad to deliver customer business outcomes.
In this role, you will possess an outcome-focused mindset and strong technical depth. You will demonstrate strong executive presence and a relentless focus on execution and bringing business value to our customers. You will have a platform engineering and technical leadership background with a product mindset, allowing you to seamlessly translate business trade-offs to C-suite executives and discuss system architectures with team members and customer stakeholders. You will know how to navigate ambiguity, mitigate technical risks, and drive high user adoption for AI technologies.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $183000 - $266000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Define the AI deployment goal with strategic customers, qualify opportunities, and drive the end-to-end lifecycle to deliver key business outcomes.
- Translate business objectives into technical delivery plans, orchestrating day-to-day execution for forward-deployment and implementation teams.
- Lead high-pressure alignment sessions with customer executives to navigate technical trade-offs, cost vectors, scope, and project risks.
- Transit frontier products to successful production, driving core metrics, workflow automation, and high user adoption, while traveling up to 30% for engagements.
- Extract field learnings to build repeatable templates and playbooks, translating on-the-ground insights into actionable feature requests for engineering teams.
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