StaffAIExecutionEngineer

## Accountabilities Design, build, and deploy production-grade agentic AI systems that power advisor-facing financial intelligence products. Own end-to-end delivery of AI features, including agent orchestration, memory systems, evaluation frameworks, and production hardening. Architect scalable AI workflows that ensure reliability, observability, and performance across distributed systems. Develop and implement evaluation frameworks to measure, improve, and optimize agent behavior at scale. Leverage agentic AI tools and frameworks to accelerate development velocity and improve engineering productivity. Drive best practices for AI-native engineering, including prompt design, multi-agent orchestration, and context optimization. Collaborate with data and product teams to translate complex financial datasets into meaningful AI-driven insights. Mentor engineers and contribute to raising the technical bar across AI system design and delivery. Requirements: 8+ years of software engineering experience, with deep expertise in building production-scale systems. Proven experience designing and shipping AI or machine learning systems into production environments. Strong hands-on experience with agentic AI workflows, including orchestration, memory systems, and evaluation design. Expertise with modern AI tooling and frameworks such as LangChain, LangGraph, GraphQL, and MCP. Fluency in AI-native development environments and tools (e.g., Cursor, Claude Code, Devin or similar). Strong understanding of evaluation methodologies for AI systems and performance measurement at scale. Advanced proficiency in data engineering and analysis using SQL and Python, with experience in platforms like Snowflake and dbt. Familiarity with distributed data and event-driven systems (e.g., Kafka, CDC pipelines, APIs) is highly desirable. Strong technical leadership, communication skills, and a track record of mentoring engineers and driving technical direction. Benefits: Competitive compensation range: