Principal Agentic Engineer, AI Ecosystem

Role overview

Set technical direction and build shared foundations for an organization-wide ecosystem of AI agents and developer tools. The role owns cross-team problems such as reusable agent architecture, safety and evaluation standards, and shared infrastructure, combining long-range design with hands-on implementation and prototypes.

Responsibilities - Define a technical architecture and shared primitives that support AI platforms, developer tooling, and agents used across the product development lifecycle. - Design and build foundational components such as agent execution frameworks, context and retrieval systems, evaluation harnesses, and identity and authorization for unattended agents. - Prototype and validate difficult architectural choices against real use cases, including on-call investigation workflows. - Establish standards, architecture records, and playbooks that teams can use to build compatible systems independently. - Advise on technical direction, build-versus-buy choices, and roadmaps across teams without relying on formal reporting authority. - Mentor senior engineers and help teams apply consistent practices for agent architecture, context engineering, and evaluation.

Requirements - 12 or more years of engineering experience, including substantial platform or systems architecture work; experience at consumer scale is preferred. - Demonstrated experience designing production agentic systems, including frameworks, identity models, or evaluation infrastructure. - Experience with non-human identity, service accounts, or agent authorization, or a well-reasoned design approach to these problems. - Familiarity with structured AI interfaces such as MCP-style tool definitions, JSON Schema, or OpenAPI, and contract-first tool-calling systems. - Experience building evaluation and regression frameworks with scoring, promotion gates, and CI integration. - Strong architecture judgment across services and teams, including tradeoffs in shared platform investment and AI costs. - Excellent written communication for technical standards and decision records.

Nice to have - Experience with agent orchestration or deployment platforms, connector gateways, ML infrastructure, model observability, or production AI systems. - Mobile engineering or high-throughput cloud systems experience.

Benefits and work setup - Remote-first role for employees working within the United States. - Listed benefits include employee-paid medical, dental, vision, life, and disability coverage; parental leave; a matched 401(k); flexible time off and company shutdowns; learning support; and equipment and remote-work reimbursement.