As an AI Forward Deployed Engineer (FDE) at Google Cloud, you are an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you 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. You will manage blocker to production including solving the integration complexities, data readiness issues, and state-management challenges that prevent AI from reaching enterprise-grade maturity. By embedding with strategic accounts, you serve a dual purpose: providing "white glove" deployment of complex AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.
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.
Responsibilities
- Serve as the lead developer for complex 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 (Eval) pipelines and observability frameworks to ensure agentic systems meet 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 formal product feature requests for the 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.
Qualifications
Minimum qualifications:
- Bachelor's degree in Computer Science or equivalent practical experience.
- Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, etc.) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Experience building machine learning solutions and working with technical customers.
Preferred qualifications:
- Experience working with recommendation engines, data pipelines, or distributed machine learning.
- Experience with deep learning frameworks (e.g. Tensorflow, pyTorch, XGBoost).
- Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments (e.g. Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume).
- Knowledge of LLM-native metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
- Understanding of the auxiliary practical concerns in production machine learning systems.
- Ability to implement secure agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.