Tech Stack

Description

You will design and build multi-agent systems and RAG pipelines to automate financial workflows for PE-backed portfolio companies, own the full stack from prompt engineering to production monitoring, and present architectural decisions to technical and non-technical stakeholders. This is a highly technical, hands-on role in a fast-paced team that ships end-to-end solutions in compressed timeframes.

Requirements

• 3–8+ years of software engineering experience with a notable portion in AI/ML systems, agent development, or applied LLM engineering
• Hands-on experience building production LLM applications: RAG systems, agentic workflows, tool use, multi-agent orchestration
• Proficiency in Python; experience with LangChain, LangGraph, AutoGen, DSPy, or equivalent orchestration frameworks
• Strong grasp of evaluation methodologies: designing evals, catching regressions, assessing LLM output quality
• Solid data engineering fundamentals: ETL design, SQL/NoSQL, vector databases, streaming or batch pipelines

Responsibilities

• Design and build multi-agent systems and RAG pipelines that automate financial workflows for PE-backed portfolio companies
• Own the full stack from prompt engineering and tool design through deployment and monitoring
• Practice eval-driven development: define how success is measured before you build
• Translate ambiguous client problems into engineering requirements and deliver results in compressed timeframes
• Present and defend architectural decisions to technical and non-technical audiences—including CFOs and PE operators
• Build observability and evaluation infrastructure to continuously improve production AI system quality
• Travel to client site as needed