Staff Forward Deployed Engineer, Data and AI

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 7 years of experience in software engineering, solution architecture, or technical consulting.
• 2 years of experience with state of the art Generative AI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with Generative AI-related concepts (language modeling, computer vision).
• 1 year of experience with state of the art Generative AI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with Generative AI-related concepts (language modeling, computer vision).
• Experience writing production level code in one or more programming languages.

Preferred qualifications:

• 10 years of experience in software engineering, solution architecture, or technical consulting.
• 6 years of experience designing and deploying cloud-native distributed systems, data pipelines, or AI/ML workflows in an enterprise environment.
• 3 years of experience with SQL and modern data warehousing concepts.
• 1 year of experience in prompt developing, model evaluation, and the creative application of Artificial Intelligence (AI).

About the job

As a Staff Forward Deployed Engineer (FDE), you will be at the forefront of leading Google's cutting-edge Data and Generative AI to solve real-world challenges for our most strategic customers. You will act as a partner in tandem with key customers, working to build, deploy, and optimize sophisticated Data & AI agents and solutions leveraging Google’s first class technologies.

The Forward Deployed Engineering team is a specialized product incubation engine dedicated to accelerating product market fit for our unified Data Cloud and AI capabilities. Tasked with bridging the gap between our powerful platform primitives and the cohesive, high-value solutions enterprises demand, this group operates as a "customer-zero" laboratory.

This is a high-impact role that bridges the gap between our core product engineering teams and complex customer needs. You will directly accelerate product innovation, customer success, disrupt the traditional SDLC, and shape the future of our Data & AI products.

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.

The US base salary range for this full-time position is $177,000-$263,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Act as the lead technical architect for incubation projects. Work to stitch Google’s Data and AI primitives (Vertex AI, BigQuery, Gemini) into high-value solutions like Agentic Workflows and new solutions.
• Own the resolution of ambiguous hurdles that prevent adoption. Debug integration issues, optimize inference latency, and architect security layers to turn "demos" into production-ready assets.
• Drive the "codify" phase by transforming bespoke solutions into reusable assets. Author "golden path" code repositories and reference architectures to enable the broader ecosystem to scale your work.
• Provide direction and mentorship to the squad’s builder engineers. Establish coding standards, review architectural designs, and ensure the team delivers high-quality, secure software.
• Partner with customer CTOs and Chief Data Officers to validate technical feasibility and align our proposed architectures with their existing enterprise stacks.