AI Forward Deployed Engineer

Alldus; New York (NY); On-site; USD 150,000 - 190,000; Full time

Job summary

Alldus is seeking a senior engineer to join a role that sits between customers and the central platform codebase. You will partner with PE portfolio companies to scope, design, and ship AI agents against live data, while also contributing to the core platform.

The split is roughly half forward-deployed work and half platform building. Ideal candidates have 5–10 years building production software, fluency with Python and/or TypeScript, and strong data/ETL skills.

Qualifications

• 5–10 years building production software with customer exposure.
• Fluent with modern LLM tooling, coding agents and evaluation workflows.
• Strong Python and/or TypeScript skills across the stack and data
• Ability to diagnose real problems with non-technical operators and translate into tech solutions.
• Comfortable owning high-stakes deployments end-to-end under time pressure.

Responsibilities

• Forward Deployment: embed with customers to scope problems and ship AI agents.
• Solution Engineering: translate workflows into working systems (entity resolution, agent deployment).
• Customer Trust: serve as the technical face, run demos, own outcomes.
• Rapid Prototyping: stand up POCs and pilots quickly with AI tooling.
• Feedback Loop: align field work with CTO and product roadmap.

Skills

Python

TypeScript

LLM tooling

SQL

Data wrangling

Tools

Snowflake

Databricks

BigQuery

Job description

This role lives at the intersection of the customer and the central platform codebase: you’ll work directly with PE portfolio companies to scope, build, and ship AI agents against real operational data, as well as build out the core platform. Half forward-deployed engineer, half platform builder.

What You’ll Own

• Forward Deployment: Embed with customers — PE firms and their portcos — to scope problems, design solutions, and ship AI agents against live operational data.
• Solution Engineering: Translate messy, real-world workflows into working systems: entity resolution, workflow inference, and agent deployment.
• Customer Trust: Be the technical face in the room — earn credibility with operators and GPs, run demos, and own outcomes end-to-end.
• Rapid Prototyping: Stand up POCs and pilots fast using AI-native tooling.
• Feedback Loop: Partner with the CTO and product to close the loop between what you do in the field and the central roadmap.

Ideal Background

• Experience: 5–10 years building production software, ideally with direct customer or stakeholder exposure (forward-deployed, solutions/sales engineering, or founding-team work).
• AI Nativity: Fluent with modern LLM tooling — coding agents, retrieval, evals, and agent frameworks — and shipping with them daily.
• Technical Fluency: Strong in Python and/or TypeScript, comfortable across the stack and with data (SQL, ETL, wrangling messy enterprise systems).
• Customer Instinct: Can sit with a non-technical operator, diagnose the real problem, and translate it into a scoped technical solution.
• Bias to Ship: Comfortable owning ambiguous, high-stakes deployments end-to-end under time pressure.

Bonus Points

• Startup DNA: Early hire or founder; comfortable wearing many hats and owning high-impact work endto-end.
• Data Depth: Experience with entity resolution, data unification, or building on modern warehouses/lakehouses such as Snowflake, Databricks, or BigQuery.
• Customer-Obsessed Builder: Equal parts engineer and trusted advisor — motivated by shipping things customers actually use.
• Intensity & Resourcefulness: Relentless about getting to the right answer; finds a way through when the path isn’t obvious.
• High Accountability: Communicates clearly, owns outcomes, and thrives in fast-paced, ambiguous environments.