Requirements

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 architecting AI systems on cloud platforms (e.g., Google Cloud Platform).
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 in multilingual natural language processing, including developing and deploying models for processing text in multiple languages.

Nice to Haves

Master’s or PhD in AI, Computer Science, or a related technical field.
Experience implementing multi-agent systems using frameworks like ReAct and self-reflection.
Experience debugging Agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.
Experience connecting agents to enterprise knowledge bases and optimizing RAG chunking to prevent hallucinations.
Ability to troubleshoot live, high-traffic systems during critical windows.
Ability to travel up to 50% of the time.

Serve as the lead developer for complex Conversational AI and Customer Experience (CX) applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive Return on Investment (ROI).
Architect and code conversational flows that are not just functional, but optimized for the "connective tissue" between Google’s Conversational AI products and customers’ live infrastructure, including APIs, legacy data silos, and security perimeters.
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 AAI stack, converting them into reusable modules or product feature requests for Engineering teams.
Collaborate with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

Exciting time to join Google Cloud’s Go-To-Market team.
Access to the world's most advanced AI portfolio, including frontier Gemini models and the complete Vertex AI platform.
Collaborative culture with direct access to DeepMind's engineering and research minds.
Equal opportunity and affirmative action employer with a commitment to building a representative workforce.
Global company with opportunities for efficient collaboration and communication.