Forward Deployed AI Engineer

About the role

Oscar Faye is working with a ~$30B alternative asset manager in New York to hire a Forward Deployed AI Engineer for its AI team. This is a hands-on individual contributor seat at the intersection of the firm’s technology and the people who run its businesses. You will sit with the lawyers, accountants, traders and operations teams across three very different businesses, work out what actually eats their week, build it, and then teach them to do it themselves.

Most AI roles right now ask you to go and build a platform from scratch. This one is the opposite. The platform is built, agents are deployed, and the businesses are already getting value from them. What the firm does not have is the person who takes all of that to the teams who cannot use it yet. The client is an intentionally flat, meritocratic and demanding firm of roughly 300 people, more than 100 of them technical, and the firm has an attrition rate that runs under one percent.

What you will do

• Embed with business teams across alternative asset management, a Bitcoin mining and custody platform, and a reinsurance business, and find the manual work worth removing
• Scope ambiguous problems directly with the people who have them, with no product manager layer and no specification handed to you
• Build and ship the workflow end to end, composing the firm’s existing AI platform, agents and shared primitives rather than rebuilding them
• Teach the team you built it for to extend and run it themselves next time
• Present your work to senior stakeholders and defend the decisions behind it
• Develop enough domain fluency in each business to tell the difference between what a user asks for and what they actually need
• Report to the Head of AI, who holds a Cambridge PhD in physics and previously ran engineering at an AI search company acquired by Upwork

What you bring

• 5+ years of hands-on software engineering experience, and you are still writing code today. This is firm policy, not a preference
• Something you shipped with LLMs or agents that real people use in production, not a prototype or a demo
• Enough full stack range to ship alone. The house stack is Python and FastAPI on the backend, Next.js and TypeScript on the front
• Experience building for people who are not engineers, and a clear account of how you worked out what they needed
• Communication that holds up in front of a senior audience. This is the attribute that most often decides the outcome here
• A track record of owning something the business depended on, with the autonomy to carry it from discovery through to production and support
• Judgement about what production readiness means for an AI feature, including how you know the output is right
• Bachelor’s degree in computer science, engineering or a related field

What success looks like in the first year

• The teams you worked with come back and ask for more. This is the firm’s own measure of whether the role is working
• Manual processes across at least two of the three businesses are meaningfully automated, and the people who used to do them can now extend the tools themselves
• Business stakeholders bring you their problems directly rather than routing them through the AI team
• You have enough fluency in at least one of alternative assets, digital assets or reinsurance to shape what gets built rather than only building what is asked for
• Engineers on the AI team trust your work well enough to let you ship into shared systems

Compensation and logistics

• Total compensation is highly competitive
• The compensation structure includes a guaranteed bonus paid monthly, dividend-producing equity in the holding company, and a position in the fund
• This role is on-site in the New York office five days per week. This is not negotiable
• Applicants must be authorized to work in the United States, transfers are available