Staff Software Engineer (AI)

• 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

• 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: 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, evaluation frameworks, and feedback loops to continuously improve AI output accuracy and system reliability

• Champion and embed AI-native development practices and tools (e.g., Cursor, Augment) to achieve significant 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 about how to get the most from AI-assisted development while maintaining reliability and correctness

• 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

• 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 grounded, implementation-level perspective

• Handle most cross-team conflicts and technical decisions autonomously

• 7+ years of backend and/or ML engineering experience with a trajectory of increasing technical leadership, architectural responsibility, and mentorship

• A portfolio of shipped production systems - we will ask you to walk through specific technical decisions you personally made and code you personally wrote; this is not a role for someone whose primary contribution has been directing others

• Deep expertise in Python, with strong proficiency in building production-grade backend services; experience with other server-side languages (Node/TS, Java) a plus

• Production experience building and operating AI/LLM-powered systems — such as AI SDKs, RAG pipelines, evaluation frameworks, agentic workflows, or model observability — in real systems, not just experimentation

• Experience leading technical design for complex systems, including making architecture decisions, evaluating tradeoffs, and guiding implementation across multiple engineers or teams

• Experience with relational/NoSQL databases, including schema design, performance tuning, and data modeling

• Experience building and operating cloud-native systems using AWS, Docker, Kubernetes, and infrastructure as code

• Strong understanding of CI/CD, observability, reliability, and operational excellence in production environments

• Ability to work through ambiguity, break down complex problems, and drive alignment across engineering, product, and design

• Demonstrated ability to raise engineering quality through technical standards, design reviews, mentoring, and reusable platform patterns

• Hands-on experience with AI-native development tools (e.g., Cursor, Augment); demonstrated ability to embed AI-driven practices to accelerate team velocity and code quality

• Ability to critically evaluate AI-generated code and outputs, including identifying failure modes, regressions, and edge cases

• Experience with document processing pipelines, structured extraction, or vector stores

• Familiarity with MLOps tooling, experiment tracking, or model deployment pipelines

• Background in financial document processing or fintech data pipelines

• Prior experience in a technical lead or TLM capacity on a new or early-stage product team