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
