Lead Forward Deployed Engineer

Madison-Davis, LLC; New York (NY); Presencial; USD 180.000 - 260.000; Jornada completa

Descripción de la vacante

Madison-Davis, LLC seeks a senior, hands-on Lead Forward Deployed Engineer to design and deliver AI-enabled applications for brokerage, trading, and investment workflows. You will combine Python software engineering with agentic AI development, collaborating with financial-market stakeholders to prototype rapidly and productionize secure AI solutions.

You will bring deep hands-on Python expertise, experience shipping LLM-powered apps, and a strong appreciation for regulated environments,

Formación

• 10+ years in investment, quantitative, software or AI engineering.
• Strong hands-on Python engineering and LLM delivery experience.
• Experience with agentic workflows, prompts, embeddings, and API integration.
• Direct exposure to trading desks, portfolios, or investment researchers.

Responsabilidades

• Design and implement LLM-enabled applications and intelligent agents.
• Develop production-quality Python apps, integrations, and APIs.
• Partner with trading, investment, and product stakeholders to identify AI use cases.
• Rapidly prototype, gather feedback, and improve functionality.
• Build agentic workflows supporting business activities and approvals.
• Collaborate with infra and engineering to deploy reliable production apps.
• Incorporate security, governance, and auditability into AI solutions.
• Contribute to technical decisions and AI capability adoption.

Conocimientos

Python engineering

LLM development

AI agents

RAG embeddings

API integration

Trading desks experience

Cloud infrastructure

Regulated environments

Stakeholder communication

Educación

CFA/FRM or equivalent

Herramientas

LangChain

LangGraph

LlamaIndex

MCP

Vector databases

GCP

Claude/Anthropic

Descripción del empleo

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.
• Strong hands-on Python engineering.
• 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.

PREFERRED EXPERIENCE

• 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.