Applied AI Engineer - Hybrid - NYC -$200K - $250K

RS Global Services is recruiting for a startup client developing AI solutions for institutional finance. The Applied AI Engineer will build AI infrastructure and user-facing agentic AI features, including LLM-powered research tools, agentic workflows, MCP-connected data sources, and automation layers. The role also involves full-stack application development, financial data pipelines, analytics, Kubernetes deployments, and rapid iteration with hedge fund users.

Responsibilities:

• AI-Powered Feature Development: Build LLM-powered features directly into client-facing platforms, including research intelligence tools, natural language query layers, automated summarization, and agentic workflows that fundamentally change how investment teams work
• Agentic Tooling & MCP Integration: Design and implement MCP-connected data sources, agentic pipelines, and AI orchestration layers using frameworks like Claude Code, LangGraph, Open Claw, Open Code and similar, extending client platforms with live, intelligent data access
• Full-Stack Application Development: Build end-to-end applications tailored to each client’s unique portfolio analytics, risk management, and research workflows—from backend APIs to responsive frontends
• Backend Services: Design and maintain high-performance APIs using Python (FastAPI or similar) that power client-specific data access, analytics, and AI inference
• Frontend Development: Build intuitive, responsive user interfaces in React that enable investment teams to interact with complex financial data clearly and efficiently
• Data Pipeline Development: Build and maintain ETL pipelines that handle critical financial market data—positions, securities, risk metrics, and research signals—with reliability and performance
• Financial Analytics: Implement analytics layers for performance and risk calculations using timeseries and linear algebra operations (Pandas, Polars)
• Ship Fast, Iterate Often: Deliver working software in compressed timelines, gather direct feedback from hedge fund users, and continuously improve, treating speed and quality as complementary, not competing
• Kubernetes Deployments: Be able to work fluidly with Kubernetes within each client environment to be able to ship fast and reliable
• This role also involves documenting technical designs, mentoring team members, and staying current with emerging AI frameworks, tools, and best practices