Principal Architect

RESPONSIBILITIES Experience building agentic harnesses from scratch, including capabilities such as tool use, multi-step chaining, reasoning, streaming, skills, multimodal integration, RAG, sandboxing, and state management. Experience designing, building, and operating production APIs and services, including RESTful APIs, streaming APIs, asynchronous workflows, service boundaries, versioning, authentication/authorization, error handling, and backward compatibility. Strong judgment around when to use synchronous APIs, event-driven architectures, queues, background jobs, or streaming protocols based on latency, reliability, scalability, and user experience requirements. Demonstrated ownership of production systems, including process improvements, roadmap contributions, defect resolution, uptime and availability monitoring, and operational reliability. Strong backend engineering fundamentals, with comfort working across service design, APIs, batch workflows, orchestration, observability, and production support. Familiarity with evaluation techniques such as creating and maintaining evaluation datasets, running offline regression evals, monitoring online production performance, rubric-based scoring, self-verification, human-in-the-loop review, AI-as-judge methods, and quality/reliability analysis at scale. Clear understanding of foundational machine learning and statistical concepts, including sampling, statistical significance, overfitting/underfitting, precision and recall, and quality tradeoff analysis. Experience building and orchestrating large-scale batch workflows that use foundation models, including LLMs and VLMs, as well as pretrained open-source models and deep learning models. Ability to design systems that safely and reliably automate meaningful enterprise workflows using non-deterministic AI components. Strong judgment around reliability, failure handling, observability, human review, and operational safety. Deep understanding of how to make systematic tra