Senior Forward-Deployed AI Engineer, Applied AI

Google Inc.

Boulder (CO)

On-site

USD 207,000 - 300,000

Full time

13 hours ago

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Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

401(k) with company match

Paid time off

Sick time

Maternity leave

Baby bonding leave

Holidays

Job summary

Google is seeking a Forward Deployed Engineer (FDE) within Applied AI to lead the delivery of production-grade, conversational AI solutions at customer sites. You will transform rapid prototypes into scalable, secure systems, owning end-to-end engineering—from prototypes to real-world business value and integration with live customer infrastructure.

You’ll collaborate with customers and Google's engineering teams, architect flows, build evaluation pipelines, and mentor teams on best practices

Qualifications

Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
8 years of experience with software development using Python or similar coding languages.
Experience deploying resources via Terraform or similar tools to automate the setup of agents, functions, or networking.
Experience building full-stack applications that interact with enterprise IT infrastructures and developing external customer projects.
Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).

Responsibilities

Serve as the lead developer for conversational AI and customer experience applications, transitioning from rapid prototypes to production-grade agentic workflows.
Architect and code conversational flows that are functional, and optimized for the connective tissue between Google’s conversational AI products and customers’ live infrastructure.
Build high-performance evaluation pipelines and observability frameworks to optimize agentic workloads, focusing on reasoning loops, tool selection, and reducing latency while maintaining production-grade security and networking.
Identify repeatable field patterns and technical friction points in Google’s Applied AI stack, converting them into reusable modules or product feature requests for Engineering teams.
Co-build with customer engineering teams to instill Google-grade development best practices, ensuring project success and end-user adoption.