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 and/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 and/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 and high-impact 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 .

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 rigorous 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.