Staff Applied AI Software Engineer

Monstro•New York, NY

8h•$224,000 - $264,000

About The Position

We're looking for a Staff Applied AI Software Engineer to support our AI team at the heart of the company's mission. This is a deeply technical role with real scope: you'll architect and ship systems that power AI-driven financial analysis, build the tooling and infrastructure that domain experts rely on, and set the engineering bar for how we build AI systems responsibly in a regulated industry. You'll influence how we design agents, how we validate and govern AI output, how we scale our modeling infrastructure, and how we think about auditability and compliance. This isn't a role where AI is a thin wrapper — the intelligence layer is the product.

Requirements

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

Nice To Haves

Experience designing multi-agent systems, agentic workflows, or AI orchestration layers
Background in quantitative finance, financial planning, or wealth management technology
Experience leading technical teams or acting as a force multiplier across engineering

Responsibilities

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