Forward Deployed Engineer

Nevis Wealth•New York, NY

63d•Hybrid

About The Position

Nevis is building the world’s first AI wealth management platform. In just 9 months, Nevis has grown to have over $50 billion of assets under management on its platform and is on track to have over $100 billion by the end of this year. Nevis is also backed by world leading investors and has raised a total of $40 million from Sequoia Capital, ICONIQ and Ribbit. Together it makes Nevis one of the highest funded and fastest growing Series A startups in Europe. We are looking for a Forward Deployed Engineer to be the technical counterpart for our customers and make sure every implementation succeeds. When one of the largest wealth management firms in the country decides to run on Nevis, you are the engineer in the room with them. You learn how their business actually works, translate that into what we build and configure, and own the technical side of the deployment from first workshop to a platform their advisors use every day. You work with their engineers, their operations team and their vendors, and you stay their technical point of contact long after go-live. The work spans whatever the deployment needs: integrations with the systems they already run, getting their data into our platform, configuring and deploying AI agents and workflows against their real processes, prototyping fast to show what is possible, and being the person who diagnoses it when something does not behave the way anyone expected. You will be trusted by the customer and relied on internally, so two things have to be true at once. You have to be precise, because these firms make decisions on the numbers we show them. And you have to move fast with whatever tools get you there, because the problems are novel and nobody is handing you a specification

Requirements

3+ years in a forward deployed, solutions engineering, technical implementation or similar customer-facing engineering role at a high-growth technology company
Strong engineer. Python and SQL are second nature, and comfortable working across systems, APIs and messy real-world data
Experience putting LLMs and agentic tooling into production, with judgment about where they are reliable and where they are not
Build fast. Prefer shipping something working and iterating than designing something perfect on paper
Precise about numbers. Know the difference between an honest "the data cannot answer that" and a confident wrong answer, and know which one costs more
Communicate exceptionally well with people who are not engineers. Can walk a COO through what was built and where it is weak without losing them
Low ego and high ownership. Does what needs to be done and the outcome matters more than the credit
Comfortable with ambiguity, and brings clarity to unstructured problems rather than frustration
Happy to travel and work onsite with customers
STEM degree from a top university, or equivalent demonstrated experience

Nice To Haves

Prior experience in wealth management, financial services or fintech
Familiarity with the US RIA landscape, including how large advisory firms are structured, regulated and operated
Experience with data integration, pipelines or systems migration at enterprise scale
At the frontier of using LLM-powered workflows in day-to-day work, with hands-on fluency across tools like Claude, ChatGPT, Cursor or similar

Responsibilities

Own the technical side of enterprise deployments end to end, from initial scoping through go-live and expansion
Be the customer's main technical point of contact, working alongside their engineers, operations teams and leadership
Understand how each firm operates deeply enough to translate their workflows into what Nevis builds, configures and automates for them
Design and deliver the integrations and data flows a deployment depends on, working with our engineering teams to make the pipes right
Configure and deploy AI agents and automated workflows against real customer processes, and tune them until they hold up in production
Build prototypes and demos fast, so customers can see what is possible before anyone commits to building it
Diagnose and resolve technical issues in live deployments, and be honest with customers about what the platform can and cannot do
Partner with Deployment Strategists on the commercial relationship, giving them the technical grounding that wins and grows accounts
Feed what you learn in the field back into the product, shaping the roadmap so every future customer inherits it