Founding Engineer — Aqua

The Problem

The alternative investment industry moves trillions of dollars a year through fax machines, PDFs, and manual data entry. A single subscription into a private equity fund can require 40+ pages of documents, hand-keyed across three different systems, touched by five different people, and take weeks to settle. The infrastructure is decades behind public markets — and the entire wealth management industry knows it.

Aqua is fixing this. We're the transaction and automation platform for alternative investments, used by some of the largest RIAs, broker-dealers, and asset managers in the country — including Blackstone, Apollo, Blue Owl, and JLL. We're on track to process $5B in new transactions in 2025. We're a YC-backed company with Series A funding.

The Engineering Challenge

This is not a clean-sheet build. We operate a production platform handling real money for institutional clients. The interesting engineering problems come from that constraint — you have to build fast and get it right, because the cost of getting it wrong is measured in regulatory risk and real dollars.

Here's what you'd actually be working on:

Agentic document systems. We're building LLM-powered pipelines that ingest, classify, extract, and generate complex financial documents — subscription agreements, capital call notices, K-1s. The challenge isn't getting a demo working. It's getting 99%+ accuracy on documents where a single wrong field is a compliance violation, across hundreds of fund-specific variations, with a human-in-the-loop review system that's fast enough to be useful.

Automation that replaces manual operations. Our clients' workflows are deeply manual. We're systematically identifying high-volume, high-cost operational processes and building systems that eliminate them — not by shipping a chatbot, but by building structured automation with robust error handling, audit trails, and graceful fallbacks. Every automation we ship directly reduces burn and increases margin.

Platform modernization under load. We inherited a large microservices codebase. We're migrating toward a modern, agent-friendly architecture while keeping production stable for clients processing millions in transactions. This is systems engineering, not greenfield hacking — and it requires judgment about what to rebuild, what to wrap, and what to leave alone.

What This Role Actually Looks Like

You report to the CTO. The team is small. That means your work has disproportionate impact, but it also means there's nowhere to hide. You'll own entire systems end-to-end: scoping, building, testing, deploying, and operating them in production.

In your first 90 days, you'll likely:

Ship production code in your first week
Own a critical automation pipeline that has direct, measurable impact on the business
Work directly with our operations and client service teams to understand the manual processes you're replacing — and build the thing that actually solves their problem, not the thing that looks good in a demo
Make architectural decisions that the rest of the team builds on

We're not looking for someone who needs a product spec and a Jira ticket to start working. We need someone who can look at a messy operational process, understand why it exists, figure out what "solved" looks like, and build it — then measure whether it actually worked.

You Might Be a Fit If

You've built and shipped systems that handle real money, real data, or real compliance constraints — not just prototypes
You treat testing, validation, and edge case handling as part of the work, not a separate phase someone else does
You've operated in an environment where you had to figure out the "what" and the "how," not just execute against a spec
You're comfortable working across backend systems, data pipelines, and infrastructure — and you can pick up new tools and frameworks fast when the problem demands it
You've worked with or built LLM-powered applications and understand both the potential and the failure modes
You'd rather own a hard problem at a small company than be a cog in a machine at a big one

What We're Not Looking For

Engineers who optimize for clean architecture over shipping. We care about code quality, but we care about outcomes more.
People who need extensive onboarding before they're productive. Our codebase is messy in places. You need to be comfortable navigating ambiguity and building context on your own.
Anyone who treats QA or validation as someone else's job. If you build it, you own whether it works — end to end, in production, with real users.

Compensation and Details

Location: NYC
Compensation: Competitive salary + meaningful equity. We're Series A — the equity is early enough to matter.
Stack: Python, Angular/React, Express, PostgreSQL, AWS. But we care more about engineering judgment than stack familiarity.

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