Applied Engineer

This role sits at the intersection of AI implementation and financial software. You won't just use AI tools you'll build AI-powered features directly into client platforms: LLM-driven research intelligence, agentic workflows, MCP-connected data sources, and automation layers that compress weeks of analyst work into seconds. The ideal candidate is a strong full-stack engineer who is fluent in modern AI tooling and deeply curious about how hedge funds and asset managers think, invest, and operate. Speed is a core part of the job the company delivers fully customized platforms in weeks, not months. What You'll Do: -AI-Powered Feature Development: Build LLM-powered features into client-facing platforms research intelligence tools, natural language query layers, automated summarization, and agentic workflows that 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, OpenClaw, OpenCode, and similar. -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) powering client-specific data access, analytics, and AI inference. -Frontend Development: Build intuitive, responsive UIs in React enabling investment teams to interact with complex financial data clearly and efficiently. -Data Pipeline Development: Build and maintain ETL pipelines handling 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 user feedback, and continuously improve treating speed and quality as complementary. -Kubernetes Deployments: Work fluidly with Kubernetes within each client environment to ship fast and reliably. What Required to Succeed: -3 8 years as a full-stack SWE or applied AI engineer (institutional investor or fintech) -Demonstrated record using agentic AI tooling effectively (Claude Code, Codex, MCP servers) and building user-facing products 0-to-1 -Strong Python expertise (non-negotiable; API experience with FastAPI, Flask, or Django highly preferred) -First-principles understanding of the agentic loop used within most agentic frameworks (Codex, Claude Code, OpenCode, Cline, etc.) -Effective in unstructured environments and ability to solve loosely defined problems -Genuine conviction that AI is transforming software and deep interest in how institutional investors think and use tech Company Preferences: -Institutional investor or fintech experience (Two Sigma, DE Shaw, Citadel, P72, Addepar) or other data-first/quantitative fields (health/biotech) -AI implementation experience hands-on building with LLM APIs, MCP servers, agentic frameworks (Claude Code, OpenClaw, LangChain), prompt engineering, etc.