Role Summary Design and implement agentic AI infrastructure on GCP, building configurable data analyst agents with multi-agent orchestration, knowledge graphs, security, and observability at scale. Key Responsibilities • • Build multi-agent orchestration frameworks with memory and caching layers • Design and instrument knowledge graph architectures for agent reasoning • Develop conversational analytics using Gemini Enterprise, Google ADK, and MCP • Configure BigQuery Data Agents and address data quality/deep research issues • Implement end-to-end security and observability for production agents • Build ETL/ELT pipelines and analytics-ready datasets on GCP Required Qualifications AI & Agents • • Strong understanding of LLMs, agentic AI, multi-agent orchestration, and tool-calling concepts • Experience building with at least one LLM platform (Gemini, OpenAI, Anthropic, etc.) and exposure to modern agent frameworks • Familiarity with RAG architectures, agent memory systems, and prompt engineering through projects or self-directed learning • Strong foundation in software engineering that transfers well to agent systems development Data Engineering • • Advanced SQL and BigQuery expertise • Experience building ETL/ELT pipelines and designing semantic models • Knowledge of modern cloud data warehouse architectures Software Development • • Strong Python development skills • Experience with APIs, distributed systems, Docker GCP & Operations • • Working knowledge of GCP services (Compute, Storage, Data, ML/AI) • Understanding of cloud security best practices • Experience implementing observability and monitoring Nice-to-Have • • Vector databases, Vertex AI, or knowledge graph experience • Background in internal developer platforms or analytics infrastructure