Staff ML Engineer: AI Agents for Enterprise Wealth

Storm2; New York (NY); Hybrid; USD 250,000 - 300,000; Full time

Job summary

Storm2 is seeking a Staff ML Engineer for AI Agents – Wealth Management Infrastructure in the San Francisco Bay Area (Hybrid). You will design and build an AI Agent Platform, evaluate agent quality for live financial environments, and advance LLM orchestration to production-grade reliability.

Expect enterprise-scale challenges and tight latency requirements. You will work across APIs, tooling, and multi-tenant deployment, translating the latest LLM capabilities into robust, secure workflows for

Qualifications

• 7+ years building production ML or backend systems for ML-powered products.
• Hands-on experience with LLMs, agent frameworks, or applied ML systems in production.
• Experience building evaluation or benchmarking systems for LLMs or ML.
• Strong Python and modern ML tooling.

Responsibilities

• Designing and building the AI Agent Platform: tool use, planning, memory, orchestration.
• Building evaluation and benchmarking frameworks to assess agent quality, reliability, and safety in production.
• LLM orchestration, prompt management, and workflow execution infrastructure.
• APIs and platform abstractions for enterprise and external partners.
• Self-hosted and multi-tenant deployment infrastructure with real enterprise constraints.
• Bridging new LLM capabilities into stable, production-grade financial workflows.
• Improving observability, failure handling, and reliability across agent systems.

Skills

Python

LLMs

Agent frameworks

Production ML systems

Back-end systems

Systems thinking

Tools

ML tooling

Job description

Storm2 is seeking a Staff ML Engineer for AI Agents – Wealth Management Infrastructure in the San Francisco Bay Area (Hybrid). You will design and build an AI Agent Platform, evaluate agent quality for live financial environments, and advance LLM orchestration to production-grade reliability.

Expect enterprise-scale challenges and tight latency requirements. You will work across APIs, tooling, and multi-tenant deployment, translating the latest LLM capabilities into robust, secure workflows for