ML Engineer: Build Production AI Tools for Fast-Paced Deals

Burns Sheehan; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Burns Sheehan Ltd is seeking a Machine Learning Engineer in New York to design and build internal AI tools, data pipelines, and production-ready workflows. You will collaborate with data scientists and portfolio stakeholders to ship end-to-end solutions that scale across a fast-moving private equity environment.

The role emphasizes hands-on development, ownership of end-to-end delivery, and rapid iteration on practical AI applications with foundation and open-weight LLMs.

Qualifications

• 2–3 years in a technical/build capacity at a top-tier consulting firm or 2–3 years as a software engineer with delivery experience
• Strong Python skills and backend engineering experience
• Experience with data pipelines and data infrastructure
• Experience building end-to-end applications with LLMs (agents, copilots, automation workflows)

Responsibilities

• Design and build internal tools, automations, and data pipelines that scale analyses across a broad portfolio
• Develop applied AI / LLM-powered workflows, such as agents or copilots that automate recurring diligence, reporting, or analytical tasks
• Partner with data scientists to productionize models and analyses
• Integrate with internal and external data sources and systems as needed
• Foster ship-fast engineering culture in a small, high-leverage team

Skills

Python

Data analysis

Product ownership

Stakeholder delivery

SQL

Tools

APIs

Cloud data warehouses

Data pipelines

Job description

Burns Sheehan Ltd is seeking a Machine Learning Engineer in New York to design and build internal AI tools, data pipelines, and production-ready workflows. You will collaborate with data scientists and portfolio stakeholders to ship end-to-end solutions that scale across a fast-moving private equity environment.

The role emphasizes hands-on development, ownership of end-to-end delivery, and rapid iteration on practical AI applications with foundation and open-weight LLMs.