Forward Deployed Engineer
Forward Deployed Engineer who owns the technical side of enterprise deployments for Nevis's AI wealth management platform — working onsite with large wealth firms to scope, integrate, migrate data, and configure AI agents and workflows through go-live and beyond. Core stack is Python, SQL, APIs, and LLM/agentic tooling.
- Compensation
- Not disclosed
- Location
- London, United Kingdom
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Solutions engineering
- Posted
Skills
Must have
- 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, comfortable across systems, APIs and messy real-world data
- Has put LLMs and agentic tooling into production, with judgment about where they are reliable
- Builds fast — would rather ship something working and iterate than design something perfect on paper
- Precise about numbers — distinguishes honest data limitations from confident wrong answers
- Communicates exceptionally well with non-engineers
- Low ego and high ownership
- Comfortable with ambiguity and unstructured problems
- Happy to travel and work onsite with customers
- STEM degree from a top university, or equivalent demonstrated experience
Nice to have
- Prior experience in wealth management, financial services or fintech
- Familiarity with the US RIA landscape and how large advisory firms are structured, regulated and operated
- Experience with data integration, pipelines or systems migration at enterprise scale
- Hands-on fluency with LLM-powered tools like Claude, ChatGPT or Cursor in day-to-day work
Full description
About Nevis
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.
The Role
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
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What You’ll Do
- 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