Forward Deployed Engineer, Generative AI, Geo

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree in Engineering, Computer Science, a related field, or

equivalent practical experience.

• 5 years of experience with software development using Python or similar

coding languages.

• Experience taking production-grade AI-driven solutions from conception to

launch for customers.

• Experience managing technical discovery sessions with customers.
• Experience building pipelines for structured and unstructured data using both

vector databases and Retrieval-Augmented Generation (RAG)-like architectures

to power enterprise AI solutions.

PREFERRED QUALIFICATIONS:

• Master’s degree or PhD in AI, Computer Science, or a related technical field.

• Experience implementing multi-agent systems using frameworks (e.g.,

LangGraph, CrewAI, ADK) and patterns (e.g., ReAct, self-reflection,

hierarchical delegation).

• Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and

techniques for optimizing state management and granular tracing.

ABOUT THE JOB:

As a Technical Solutions Consultant, you will be responsible for the technical

relationship of our largest advertising clients or product partners. You will

lead cross-functional teams in Engineering, Sales and Product Management to

leverage emerging technologies for our external clients/partners. From concept

design and testing to data analysis and support, you will oversee the technical

execution and business operations of Google's online advertising platforms or

product partnerships.

You will be able to balance business and partner needs with technical

constraints, develop innovative, cutting edge solutions and act as a partner and

consultant to those you are working with. You will also be able to build tools

and automate products, oversee the technical execution and business operations

of Google's partnerships, as well as develop product strategy and prioritize

projects and resources.

The Forward Deployed Engineer (FDE) for Google Maps Platform (GMP) is a

specialized engineering role that sits at the intersection of product

development and customer implementation. Unlike traditional engineers who focus

primarily on internal product cycles, FDEs work "at the front lines," embedding

themselves within the technical ecosystems of our most complex partners. Your

mission is to ensure that Google's geospatial technology solves real-world

business problems at scale.

By embedding with accounts, you will serve a dual purpose: providing "white

glove" deployment of complex Geospatial AI systems and acting as a critical

feedback loop, transforming real-world field insights into Google Maps Platforms

future product roadmap.

The Geo team is focused on building the most accurate, comprehensive, and useful

maps for our users, through products like Maps, Earth, Street View, Google Maps

Platform, and more. Every month, more than a billion people rely on Maps

services to explore the world and navigate their daily lives.

The Geo team also enables developers to use the power of Google Maps platforms

to enhance their apps and websites. As they plot a course for the future of

mapping, they are solving complex computer science problems, designing beautiful

and intuitive product experiences, and improving our understanding of the real

world.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $152000 - $221000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• Serve as a developer for complex AI applications, transitioning from rapid

prototypes to production-grade agentic workflows (e.g., multi-agent systems,

Model Context Protocol (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 as part of an expert team.

• Build high-performance evaluation pipelines and observability frameworks to

ensure agentic systems meet requirements for accuracy, safety, and latency.

• Identify repeatable field patterns and friction points in Google’s AI stack,

converting them into reusable modules or formal product feature requests for

engineering teams.

• Co-build with customer engineering teams to instill Google-grade development

best practices, ensuring long-term project success and high end-user

adoption.