Job Description

Information Security Responsibilities

• Promote and enforce awareness of key information security practices, including acceptable use of information assets, malware protection, and password security protocols
• Identify, assess, and report security risks, focusing on how these risks impact the confidentiality, integrity, and availability of information assets
• Understand and evaluate how data is stored, processed, or transmitted, ensuring compliance with data privacy and protection standards (GDPR, CCPA, etc.)
• Ensure data protection measures are integrated throughout the information lifecycle to safeguard sensitive information

WHAT YOU'LL DO

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
apabilities
• 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.

Preferred

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