Technical Deployment Lead, Google Cloud
About the role
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.
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.
Requirements
- 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.
Benefits
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
Pay
$183000 - $266000 (USD) + 20% bonus target + equity + benefits
Schedule
Full-time