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💼 Full-time🗓 2026-06-25

Core

Drive technical direction and architecture for AI systems, including shared AI SDKs, guardrails, and agentic workflow infrastructure, while delivering hands-on full-stack contributions.

Role type

Principal Engineer (AI Systems & Architecture)

Builds

Production-grade AI platforms, LLM-powered systems (RAG, agents), and enterprise accounting software capabilities.

Domain

Enterprise Software / Artificial Intelligence / Large Language Models

Deliverable

production ML models | product features | infrastructure

Required skills

AI system architecture, LLM integration (RAG, agents), full-stack development, technical strategy, team mentorship, cross-functional leadership, production code delivery, AI code evaluation, system reliability, stakeholder alignment

Preferred skills

Enterprise API design, team leadership experience, object-oriented programming expertise

Technologies

LLMs, RAG pipelines, agentic workflows, AI SDKs, Cursor, Augment

Responsibilities

Set technical direction for AI systems and backend platforms, design and operate LLM-powered systems, write production code as a hands-on IC, mentor engineers and establish coding standards, collaborate cross-functionally to translate requirements into technical plans, evaluate and select technologies for scalability

Seniority

Principal, hands-on IC with strategic leadership

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## About the Role As a Principal Engineer, you will drive the team's technical direction and play a pivotal role in designing and architecting accounting software systems. This role involves mentoring team members, solving complex technical challenges, and delivering hands on contributions as a full stack leader. You will work closely with engineers and cross functional partners across Product, Design, and Quality Assurance to expand system capabilities. Your leadership will be instrumental in achieving the team's objectives and advancing our technical vision. ## What You'll Do ### Own Technical Direction and Architecture - Set the technical direction for AI systems, including shared AI SDKs, guardrails, evaluation frameworks, feedback systems, and agentic workflow infrastructure - Own architecture and technical strategy for complex backend and AI platform systems, from design through production - Lead technical design for ambiguous, cross functional initiatives, evaluating tradeoffs, aligning stakeholders, and driving implementation - Evaluate and select technologies with a bias toward what ships well and scales sustainably ### Build and Operate AI Systems - Write production code as a hands on individual contributor, this is not a role that delegates implementation to others - Design and operate LLM powered systems, including RAG pipelines, agentic workflows, evaluation infrastructure, guardrails, and model observability - Own end to end reliability of AI systems, from design through structured output delivery - Define quality benchmarks ### Champion AI Native Development - Champion and embed AI native development practices and tools, such as Cursor and Augment, to drive meaningful productivity gains across the team - Foster a culture of rapid iteration, high velocity, and quality, including guiding the effective use of AI code generation - Bring strong, informed opinions on how to get the most from AI assisted development while maintaining reliability and correctness ### Lead and Grow the Team - Mentor engineers, raise the quality of technical decision making, and help the team execute with consistency - Establish coding standards, review practices, and architectural documentation that scale as the team grows - Help define what "good" looks like for a team building at speed without sacrificing quality - Partner with recruiting to build and grow the team ### Collaborate Cross Functionally - Work closely with Engineering Managers, Product, Design, and QA to translate requirements into executable technical plans - Participate actively in design reviews and roadmap discussions with a grounded, implementation level perspective - Handle most cross team conflicts and technical decisions autonomously ### AI Fluency - Ability to critically evaluate AI generated code and outputs, including identifying failure modes, regressions, and edge cases introduced by AI assisted development - Experience building and shipping production grade software using AI assisted workflows across the full SDLC - Hands on experience developing or integrating LLM powered systems, such as agents, copilots, tool using workflows, or multi step reasoning systems - Familiarity with patterns such as tool calling agents, planning and execution loops, and retrieval augmented generation (RAG) - Demonstrated ability to leverage modern AI tools to improve development velocity, code quality, and problem solving - Experience contributing to AI powered features, such as intelligent search, conversational interfaces, recommendations, and automation - Working knowledge of LLMs, embeddings, semantic search, and RAG pipelines - Ability to identify and evaluate opportunities to integrate AI capabilities into products and workflows ## Qualifications - Bachelor's degree in Computer Science or equivalent work experience - 13 to 17 years of experience in software development across building, integration, security, and architecture - Previous experience building enterprise grade APIs is a plus - Previous experience leading a team is a plus - Expertise in object oriented programming
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