Lead Forward Deployed Engineer

Madison-Davis, LLC; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

Madison-Davis, LLC seeks a senior, hands-on Lead Forward Deployed Engineer to design and deliver AI-enabled applications supporting brokerage, trading and investment workflows in New York. You will blend Python software engineering with agentic AI development and work closely with financial-market stakeholders to identify opportunities and rapidly develop prototypes.

The role requires strong engineering fundamentals, experience with LLM development, and an understanding of financial markets.

Qualifications

• 10+ years of investment/software/AI engineering experience.
• Experience delivering LLM-enabled applications and AI agents.
• Knowledge of RAG, embeddings, agentic workflows, prompt engineering, and API integration.
• Direct experience with trading desks, portfolio managers, or investment researchers.
• Familiarity with brokerage, trading, or investment workflows and regulated environments.

Responsibilities

• Design and implement LLM-enabled applications and intelligent agents.
• Develop production-quality Python applications, integrations, and APIs.
• Partner directly with trading, investment, and product stakeholders to identify valuable AI use cases.
• Rapidly prototype solutions, gather feedback, and improve application functionality.
• Build agentic workflows that support business activities and appropriate approval processes.
• Collaborate with infrastructure and engineering teams to deploy reliable production applications.
• Incorporate security, compliance, governance, and auditability into AI solutions.
• Contribute to technical decisions and ongoing adoption of AI capabilities.

Skills

LLM Development

Python

API Integration

Stakeholder Communication

Security & Compliance

Education

Bachelor's degree in CS/Engineering

Tools

LangChain

LangGraph

Vector Databases

Job description

A global financial services organization is seeking a senior, hands-on Lead Forward Deployed Engineer to develop AI-enabled applications supporting brokerage, trading, and investment workflows.

The position combines Python software engineering, agentic AI development, and direct product and business collaboration. This individual will work closely with financial-market stakeholders to identify opportunities, quickly develop working prototypes, and deliver secure, scalable AI applications.

The successful candidate will bring strong engineering fundamentals, hands-on LLM development, and an understanding of financial markets.

Responsibilities

• Design and implement LLM-enabled applications and intelligent agents.
• Develop production-quality Python applications, integrations, and APIs.
• Partner directly with trading, investment, and product stakeholders to identify valuable AI use cases.
• Rapidly prototype solutions, gather feedback, and improve application functionality.
• Build agentic workflows that support business activities and appropriate approval processes.
• Collaborate with infrastructure and engineering teams to deploy reliable production applications.
• Incorporate security, compliance, governance, and auditability into AI solutions.
• Contribute to technical decisions and ongoing adoption of AI capabilities.

Role Requirements

• 10+ years of combined relevant investment, quantitative, software, or AI engineering experience.
• Experience delivering LLM-enabled applications and AI agents.
• RAG, embeddings, agentic workflows, prompt engineering, and API integration.
• Direct experience working with trading desks, portfolio managers, or investment researchers.
• Knowledge of brokerage, trading, or investment workflows.
• Fluency in at least one financial asset class.
• Cloud infrastructure and modern engineering practices.
• Experience delivering secure technology within regulated environments.
• Excellent stakeholder communication and ability to translate business needs into software.

Nice to Have

• Claude / Anthropic and GCP experience.
• LangChain, LangGraph, LlamaIndex, MCP, and vector databases.
• Desk strategy or quantitative technology background.
• Experience with internal AI assistants or document intelligence.
• CFA, FRM, or comparable financial-market credentials.