Lead Python AI Engineer

We are seeking a Lead AI Engineer to design and deliver production-grade AI agents and data-integrated tool APIs in a cloud-native Python environment. You will shape agent architecture, ensure reliable observability and schema-compliant access patterns, and collaborate with distributed stakeholders to ship high-quality outcomes. Design production-grade AI agent architectures and tool interfaces aligned to platform standards Build scoped AI agents with tool-calling, structured outputs, and robust prompt/tool orchestration Implement read-only Model Context Protocol servers that securely expose enterprise datasets to agents Develop FastAPI services and asynchronous task handlers to support low-risk actions and ticket generation Write and optimize SQL for normalized cost datasets while respecting partitioning and clustering constraints Implement health and readiness probes, structured JSON logging, and distributed tracing for observability Establish unit and integration tests co-located with capability modules to protect critical flows Coordinate peer code reviews and enforce monorepo structure and engineering excellence practices Collaborate with product and architecture stakeholders to refine requirements and deliver measurable results 5+ years of production software development experience with Python 2+ years of hands-on experience building LLM integrations or AI agents Lead-level technical leadership experience guiding design and code quality Strong project ownership skills for delivering production-grade services end to end Advanced Python 3.11+ skills with AsyncIO and clean architecture practices Strong FastAPI skills for building backend microservices and tool APIs Strong SQL skills for authoring and optimizing analytical queries Solid Docker skills for packaging and running services reliably Hands-on Google Agent Development Kit experience for agent and tool development Model Context Protocol experience implementing safe, read-only data access patterns Upper-Intermediate English proficiency (B2) for cross-team collaboration Google Cloud BigQuery experience optimizing large partitioned datasets Google Vertex AI experience integrating hosted models and evaluation workflows CI/CD experience building automated test and release pipelines Terraform experience provisioning and managing cloud infrastructure as code LangChain experience building agent workflows and tool integrations