Lead AI Engineer creating AI - powered solutions with Python and machine learning frameworks. Collaborate with data scientists and engineers to optimize innovative application development.
About the role
- AI Engineer designing and building agentic systems for audit workflows. Collaborating closely with product and design teams in a hybrid work environment.
Responsibilities
- Design, build, and ship agentic systems that automate and augment complex audit workflows
- Translate customer problems into concrete agent behaviors and workflows
- Integrate and orchestrate LLMs, tools, retrieval systems, and logic into cohesive, reliable agent experiences
- Own agents in production, including performance and observability
- Use AI as core leverage in how you design, build, test, and iterate
- Prototype quickly to resolve uncertainty, then harden systems for enterprise-grade reliability
- Build evaluations, feedback mechanisms, and guardrails so agents improve over time
- Design prompts, retrieval pipelines, and agent orchestration systems that perform reliably at scale
- Make tradeoffs about what to build, what to cut, and what not to build at all
- Partner closely with Product and Design to define agent capabilities that drive real customer outcomes
- Stay deeply connected to how customers actually use agents and optimize for the highest impact problems
- Identify the highest-leverage capability gaps and unblock them without waiting for direction
- Take full ownership of large product areas rather than executing on narrow tasks
- Identify bottlenecks and unblock progress without waiting for direction
- Increase team velocity by building reusable abstractions, tools, and patterns for agent development
Requirements
- Multiple years of experience shipping production software in complex, real-world systems
- Experience with TypeScript, React, Python, and Postgres
- Built and deployed LLM-powered features serving production traffic
- Designed retrieval pipelines and agent orchestration systems
- Implemented evaluation frameworks for model outputs and agent behaviors
- Worked with vector databases, embedding models, and RAG architectures
- Hands-on experience with modern LLM APIs (OpenAI, Gemini, Anthropic, etc.) and agent frameworks
- Comfort operating in ambiguity and taking responsibility for outcomes
- Deep empathy for professional-grade, mission-critical software (experience with audit and accounting workflows are not required)
Benefits
- Competitive compensation packages with equity ownership
- Comprehensive health and wellness benefits
- Flexible time off and work schedules
- Technology reimbursements
- 401(k) plan
- Twice-yearly in-person offsites across the U.S.
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