Original listing text, shown exactly as published by the company.
What you'll do
Build and Operate AI Systems
- Design, build, and ship production-quality backend services, APIs, and AI platform components used across multiple engineering teams
- Build and integrate LLM-powered systems such as RAG pipelines, AI SDKs, evaluation workflows, guardrails, prompt/tool orchestration, and model observability
- Improve the reliability, scalability, observability, and operational quality of production AI systems
- Build internal tools, frameworks, automation, and documentation that improve developer productivity and AI capabilities
- Participate in code reviews, design reviews, debugging, incident response, and operational support
Drive Technical Excellence
- Contribute to technical design for complex projects, including evaluating tradeoffs and proposing pragmatic implementation plans
- Partner with product, design, and engineering teams to translate platform needs into well-designed technical solutions
- Help identify and reduce technical debt, reliability risks, and friction in the software development lifecycle
- Collaborate with Staff and senior engineers to establish reusable patterns and raise engineering standards
Build with AI-Native Practices
- Use agentic coding tools and LLM-assisted development as a primary part of your workflow — this is how the entire team operates
- Critically evaluate AI-generated code for correctness, edge cases, and regressions — shipping quality output regardless of how it was produced
- Contribute to the team's evolving practices around AI-accelerated development and testing
Qualifications
Required
- 5+ years of experience designing, building, and operating production software systems
- Strong backend engineering experience with Python frameworks such as FastAPI, Flask, or Django
- Experience building or integrating AI/LLM-powered systems in production - such as RAG pipelines, AI SDKs, evaluation workflows, guardrails, or agentic workflows
- Experience with relational/NoSQL databases, including schema design, query optimization, and data modeling
- Experience with cloud-native technologies such as AWS, Docker, and Kubernetes
- Strong understanding of CI/CD, observability, and operating services in production
- Ability to break down complex technical problems and deliver pragmatic, maintainable solutions
- Strong ownership mindset, with the ability to drive projects independently while collaborating effectively
- Clear communication skills and the ability to explain technical tradeoffs to engineering and cross-functional partners
- Hands-on experience with AI-native development tools (e.g., Cursor, Augment); demonstrated ability to embed AI-driven practices to accelerate velocity and code quality
- Ability to critically evaluate AI-generated code and outputs, including identifying failure modes, regressions, and edge cases
Preferred
- Experience with document processing pipelines, structured extraction from unstructured documents, or vector stores
- Familiarity with evaluation frameworks for LLM output quality (e.g., RAGAS, custom evals, human-in-the-loop review)
- Background in financial services or fintech
CompensationCompensation for this position includes a base salary, equity, and a variety of benefits. The U.S. base salary range for this role is $185,000 – $225,000 USD. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable.
