Job Description
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Company Overview
A growing technology company is focused on redefining accounting software for startups and mid-size technology companies by developing modern, AI-powered financial solutions. The organization is backed by prominent investors and has rapidly expanded its customer base since graduating from the Summer 2023 cohort of Y Combinator.
Key Responsibilities
- Design, build, and operate production AI agents that perform real-world accounting and financial workflows.
- Develop maintainable Python code and robust backend systems.
- Design APIs, data models, distributed systems, and testing strategies for production applications.
- Develop LLM-powered agents using capabilities such as tool calling, orchestration, retrieval, and application data integration.
- Build agentic workflows that interact with application data and customer-facing systems.
- Establish and maintain agent evaluation processes to measure quality and reliability in production.
- Implement tracing, monitoring, and systematic debugging processes to identify and resolve agent failures.
- Design appropriate permissions and data-access controls for AI agents.
- Develop safeguards for human approvals, recovery from partial failures, and actions performed on behalf of users.
- Evaluate tradeoffs between accuracy, latency, cost, and system complexity.
- Determine when a problem is best addressed through an AI agent, deterministic workflow, or combination of both.
- Lead complex technical initiatives and make architectural decisions that influence multiple teams.
- Mentor engineers and contribute to improving engineering standards and development practices.
- Collaborate closely with engineering, product, and design teams to deliver customer-focused solutions.
- Take ownership of projects from prototyping and experimentation through deployment, monitoring, and production optimization.
Required Qualifications
- Strong software engineering background with substantial experience designing, building, and operating production backend systems.
- Strong proficiency in Python and experience writing maintainable production code.
- Hands-on experience developing and deploying LLM-powered agents used by real customers.
- Experience with agent capabilities including tool calling, orchestration, retrieval, and integration with application data and workflows.
- Experience evaluating and improving AI agent quality in production.
- Knowledge of tracing, monitoring, evaluations, and systematic debugging of AI systems.
- Practical experience designing agent permissions, data access, human approval processes, and failure recovery mechanisms.
- Strong understanding of distributed systems, APIs, data models, and software testing.
- Ability to evaluate tradeoffs involving accuracy, latency, cost, and technical complexity.
- Experience determining when to use AI agents, deterministic workflows, or hybrid approaches.
- Demonstrated ability to lead complex engineering initiatives and make architectural decisions across teams.
- Experience mentoring engineers and contributing to engineering standards.
- Ability to work effectively in a lean, fast-moving environment.
- Strong ownership mentality and ability to take projects from initial prototype through production deployment and monitoring.
Ideal Candidate
- Experienced software engineer who has owned significant production systems and can explain the technical tradeoffs, failures, and lessons behind shipped products.
- Has taken AI agents beyond demonstrations and prototypes and improved them through real-world customer usage.
- Demonstrates curiosity about accounting and financial workflows.
- Has prior fintech or accounting experience, which is considered a plus.
- Cares deeply about customer outcomes and building reliable products.
- Communicates technical decisions clearly and collaborates effectively with engineering, product, and design teams.
- Enjoys mentoring other engineers and contributing to team-wide engineering improvements.
- Wants the ownership and scope associated with a staff-level engineering role while remaining actively involved in building and shipping software.
- Comfortable experimenting, iterating, and delivering quickly in an agile environment.
- Takes end-to-end ownership of AI systems, from model prototyping through production deployment and monitoring.
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