Principal Architect

Job Description

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 tradeoffs between quality, reliability, latency, cost, explainability, and user experience in complex AI systems.
• Ability to design pragmatic architectures that balance innovation with production readiness.
• Comfort working in environments where model behavior is non-deterministic and system design must account for uncertainty, evaluation, monitoring, and graceful failure.
• Experience with both lexical and embedding-based search methods.
• Ability to reason about relevance, ranking, latency, recall, precision, indexing strategy, and retrieval performance.
• Experience working with foundation models and open-source LLMs beyond simple API calls.
• Familiarity with lower-level model behaviors and controls, including temperature, top-p sampling, logprobs, confidence scoring, prompting strategies, and model selection tradeoffs.
• Comfort and willingness to build front-end experiences using JavaScript/TypeScript and frameworks such as React.
• While the role is primarily backend and AI systems focused, the ability to contribute to user-facing product experiences is important.
• Willingness to work across the stack and own the end-to-end product experience is essential.

Familiarity with tools such as FFmpeg, OpenCV, or similar media-processing libraries is a plus.

Qualifications

QUALIFICATIONS/REQUIREMENTS

• 7+ years of a programming languages such as Java, Golang, Python, JavaScript
• 2+ years of experience architecting, designing, and optimizing search and information retrieval systems at scale.
• 6 months to 1 year of hands-on experience building agentic products or solutions, including tool-using agents, conversational agents, long-running agents, reasoning/planning ag…