Architect and build AI agent systems — including multi-agent workflows, orchestration layers, and the infrastructure that makes them reliable and auditable in production
Design modeling and reasoning systems that meet the correctness and explainability requirements of financial services — where every recommendation needs to be traceable and defensible
Build internal tooling platforms (Python/FastAPI) that enable domain experts and engineers to author, validate, and deploy AI-powered analysis at scale
Own distributed backend infrastructure: event-driven workers, job pipelines, and multi-stage processing systems that deliver AI outputs reliably under load
Establish patterns for AI output validation and quality assurance — including automated evaluation pipelines, hallucination detection, and compliance guardrails
Drive technical decisions across teams: schema design, API contracts, system boundaries, and service architecture
Mentor senior engineers and raise the engineering bar across the organization

8+ years of engineering experience, with a track record of leading complex systems from design to production
Hands-on experience with AI/ML systems — agent architectures, LLM integration, model evaluation, or AI pipelines in production
Python expertise — async services, type-safe code, and systems that scale
Experience building AI systems in regulated industries (financial services, healthcare, legal) or with compliance, auditability, or explainability requirements
Strong distributed systems fundamentals: event-driven architectures, message queues, job state machines, and worker patterns
Relational database fluency and an instinct for data modeling
Full-stack range — you can build product-quality React/TypeScript UIs when the problem calls for it
Opinionated about how AI systems should be tested, evaluated, and monitored in production.

Competitive salary
Equity
Robust benefits package, including paid health, vision, dental, and disability coverage

Staff Applied AI Software Engineer

Key skills