Principal AI Agent Product Engineer

Required Qualifications:

•            10+ years of software engineering, platform engineering, architecture, or closely related experience.

•            Experience operating at a Principal Engineer, Principal Architect, Staff+, or equivalent senior technical leadership level.

•            Proven experience building, scaling, or leading enterprise software or data platforms.

•            Strong software engineering foundation with architectural and systems-thinking depth.

•            Demonstrated experience with agentic AI development, agent architecture, and agent optimization in real-world solutions.

•            Strong understanding of agent ecosystems, orchestration patterns, platform tradeoffs, and enterprise adoption considerations.

•            Hands-on experience delivering and supporting solutions in Google Cloud Platform production environments.

•            Experience with enterprise data platforms, including data ingestion, data access, governance, and knowledge management patterns.

•            Experience shaping product vision, defining technical strategy, and building cross-functional roadmaps.

•            Ability to translate ambiguous business needs into clear technical direction and executable initiatives.

•            Excellent communication, stakeholder management, prioritization, and influencing skills.

•            Ability to engage credibly as a peer with architects, principal engineers, senior developers, business leaders, and executives.

Preferred Qualifications:

•            Google Cloud certification, ideally Professional Cloud Architect, Professional Data Engineer, Professional Machine Learning Engineer, or another relevant professional-level credential.

•            Working awareness of Google Agent Development Kit (ADK) concepts and the broader Google AI ecosystem. Deep ADK specialization is not required.

•            Experience with BigQuery and modern cloud data platforms.

•            Experience designing or supporting agent registries, agent orchestration frameworks, or multi-agent systems.

•            Experience with knowledge graphs, knowledge catalogs, or enterprise knowledge platforms.

•            Experience evaluating build-versus-buy decisions for AI platform capabilities.

•            Experience driving adoption of enterprise AI-enabled products and services.