Requirements

Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
5 years of experience with software development using Python or similar coding languages.
Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., GCP).
Experience building pipelines for structured and unstructured data using vector databases and RAG-like architectures to power enterprise AI solutions.
Experience managing technical discovery sessions.

Nice to Haves

Master’s degree or PhD in AI, Computer Science, or a related technical field.
Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) 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 complex AI applications, transitioning rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) driving measurable ROI.
Architect and code the "connective tissue" between Google AI products and customers' live infrastructure, including APIs, legacy data silos, and security perimeters.
Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet accuracy, safety, and latency requirements.
Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal 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.

Opportunity to work with 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 teams.
Play a critical role in shaping Google Cloud’s future product roadmap and driving customer success in the AI domain.