Field Solutions Architect, Generative AI
Job Overview
Who's the hiring manager?
Sign up to PitchMeAI to discover the hiring manager's details for this job. We will also write them an intro email for you.

Job Description
Field Solutions Architect, Generative AI
Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.
Applicants in the County of Los Angeles: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
The application window will be open until at least February 26, 2026. This opportunity will remain online based on business needs which may be before or after the specified date.
In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:
- Health, dental, vision, life, disability insurance
- Retirement Benefits: 401(k) with company match
- Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
- Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)
- Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
- Baby Bonding Leave: 18 weeks
- Holidays: 13 paid days per year
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: San Francisco, CA, USA; Boulder, CO, USA; Cambridge, MA, USA; Chicago, IL, USA; Mountain View, CA, USA; New York, NY, USA; Reston, VA, USA; Seattle, WA, USA; Sunnyvale, CA, USA; Los Angeles, CA, USA; Washington D.C., DC, USA.
Minimum qualifications
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 6 years of experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, Typescript or comparable languages.
- Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI and hardware infrastructure requirements.
- Experience architecting scalable AI systems on cloud platforms e.g., Google Cloud Platform (GCP).
- Experience building pipelines for structured, unstructured data, incorporating vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise-grade AI solutions.
Preferred qualifications
- Master’s or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s ADK) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of LLM-native metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
About The Job
As a Generative AI Field Solutions Architect at Google Cloud, you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. You will function as a builder-consultant, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment.
This role is designed for high-agency engineers with a founder’s mindset. You will manage blockers to production, including solving integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you will serve a dual purpose: providing white glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.
It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
Responsibilities
- Serve as the lead developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable return on investment.
- Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
- Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
- Identify repeatable field patterns and technical friction points in Google’s AI stack, converting them into reusable modules or product feature requests for the Engineering teams.
- Drive engineering excellence by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to uplevel organizational technical capabilities.
Key skills/competency
- Generative AI
- Cloud Architecture
- Python/Typescript
- Google Cloud Platform
- Agentic Systems
- Data Pipelines
- RAG Architectures
- Technical Consulting
- System Integration
- Product Development Feedback
How to Get Hired at Google
- Research Google's AI strategy: Understand Google Cloud's vision for Generative AI and its Vertex AI platform.
- Tailor your resume: Highlight experience in building and shipping production-grade AI solutions with Python/Typescript and cloud platforms.
- Showcase GenAI expertise: Emphasize projects with multi-agent systems, RAG architectures, and LLM optimization techniques.
- Prepare for technical deep-dives: Expect questions on scalable AI system architecture, data pipelines, and agentic workflows.
- Demonstrate customer focus: Be ready to discuss how you've translated business needs into technical solutions for external clients.
Frequently Asked Questions
Find answers to common questions about this job opportunity
Explore similar opportunities that match your background