Requirements

Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
8 years of experience in cloud computing or a technical customer-facing role.
Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., Google Cloud Platform).
Experience building pipelines for structured and unstructured data using vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise AI solutions.
Experience leading technical discovery sessions.

Nice to haves

Master’s degree or PhD in Artificial Intelligence, Computer Science, or a related technical field.
Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, Agent Development Kit) and complex 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.

Serve as a developer for AI applications, moving from rapid prototypes to production-grade agentic workflows that drive measurable ROI.
Architect and code integration layers between Google’s AI products and customer infrastructure, including APIs, legacy data silos, and security perimeters.
Build high-performance evaluation pipelines and observability frameworks to ensure AI systems meet strict accuracy, safety, and latency requirements.
Identify and convert repeatable field patterns and friction points into reusable modules or formal product requests for Engineering teams.
Collaborate closely with customer engineering teams to embed Google-grade development best practices, ensuring long-term project success and adoption.