Principal AI Architect

Role overview Set system architecture for production agentic AI capabilities as a senior individual contributor working across engineering, product, and data science. The focus is on how agents are orchestrated, how they manage context and state, how they use tools, and how they operate reliably alongside existing platform services. The architect prototypes to validate key decisions and guides teams through design standards and reviews rather than owning full feature implementation.

Responsibilities - Define architecture for agent orchestration, context and state, tool boundaries, and reliability in non-deterministic systems. - Write technical design reviews, design documents, and standards that guide implementation across teams. - Build prototypes to test architectural choices before teams adopt them at scale. - Partner with data science to integrate model and agent work with APIs, event streams, storage, and platform services. - Review designs across engineering, surface technical risks, and build agreement around architectural direction. - Help shape AI-assisted and agentic development practices, mentor senior engineers, and explain design trade-offs to technical and executive audiences.

Requirements - At least 10 years of software engineering experience, including substantial work as a senior, staff, or principal architect or technical lead for production systems. - Deep distributed systems expertise, including service boundaries, consistency, event-driven design, API design, and reliable operation at scale. - Production experience architecting or prototyping LLM-powered or agentic systems, including agent loops, context engineering, tool or function calling, MCP, retrieval-augmented generation, state and memory, and reliability safeguards. - Working knowledge of data modeling, pipelines, and streaming versus batch processing sufficient to make architectural decisions. - Evidence of influencing engineers without formal authority, reviewing unfamiliar systems rigorously, and producing clear technical documents. - Strong mentorship and communication skills, including the ability to explain complex decisions and trade-offs.

Nice to have - Experience guiding AI-assisted or agentic development across an engineering organization. - Experience working closely with data science or machine learning teams to productionize their work. - Cybersecurity industry experience. - Familiarity with technologies such as Node.js, TypeScript, PostgreSQL, ClickHouse, Kafka, AWS, Kubernetes, Terraform, Helm, or ArgoCD; knowledge of every listed tool is not required.

Benefits and work setup The role is hybrid for employees within 75 miles of offices in Midtown New York City or downtown Austin, with 1–2 onsite days per week; employees outside both areas may work fully remotely. Benefits vary by country and include stock options, health coverage, paid time off, parental leave, and tuition reimbursement. Listed estimated total compensation, including bonus, is $270,000–$330,000 USD in New York, $240,000–$300,000 USD in Austin and other U.S. locations, and $250,000–$350,000 CAD in Canada.