Forward Deployed Engineer

We started Parsio with one intention: help make European industry competitive for the 21st century. It’s a domain where deep technical skill still turns into real, measurable impact, and that’s the leverage we want.

What we do: help procurement teams buy better and buy smarter. Manufacturers sit on huge piles of unstructured technical data (PDFs, scanned drawings, CAD files, spreadsheets). We turn it into something a human and an AI can both understand, then build a cost modeling layer on top that can simulate anything: a supplier change, a part redesign, a commodity price hike, and more. Weeks of expert work become a cost-saving strategy a team can act on.

We’re a small, synchronous, trust-first team in Paris: side by side most of the week, decisions made out loud, no org chart between you and the two founders.

Why we’re hiring for this role

AI is easy to try. Making it work inside an industrial enterprise is a different problem entirely. The gap between a demo that impresses a procurement director and a system their team actually sources on is where deals are won or lost, and today that gap is crossed by the two founders.

What we’re building wasn’t possible six months ago. The bottleneck was never insight, it was capacity, and that constraint just disappeared. This is an 18-month window to build the category winner in AI-native procurement intelligence for industry, and the teams that deploy fastest now will lock in advantages the incumbents can’t catch.

We’re hiring the person who takes clients beyond the POC and into production, and who turns everything learned in the field back into the platform.

The role

You’re a full-stack engineer who works directly inside the customer’s environment, alongside their buyers, engineers and operators. You report to the Co-founder & CTO and work daily with the CEO.

You operate like a startup CTO embedded in the account: you own end-to-end execution of a high-stakes deployment. In practice:

• You write real production code. This is an engineering role, not a solutions-consulting one.
• You integrate with legacy systems and brittle data pipelines: ERPs, PLMs, supplier portals, twenty years of Excel.
• You model messy institutional workflows: how this client actually sources, costs and decides.
• You build specialized skills, workflows and integrations on top of Parsio, so our agents behave like a highly trained teammate for this client, not a vanilla chatbot.
• You sit alongside analysts, operators and decision-makers and adapt continuously as requirements evolve.

This work is intentionally uncomfortable. It exposes you to edge cases, organizational friction and failure modes that never surface in a controlled product environment.

The feedback loop. You are the closest person to the customer, and what you learn flows straight back into the platform: you deploy into a real institution, solve a concrete operational problem that delivers measurable savings, identify the structural challenges that recur across clients, abstract them into reusable primitives, and incorporate those into core Parsio. The goal isn’t one heroic deployment. It’s making the next one cheaper because of what you fed back from this one.

How we ship. Every change goes through review and CI before it merges; tests, evals and monitoring are part of “done”, not a follow-up. Because we trust our CI, we deploy to production whenever we need to.

The stack

The product is made of several blocks with distinct roles:

• App client: An SPA, the user-facing UI.
• App backend: A REST API with direct DB access; it serves the SPA and the AI backend, and computes the cost models.
• AI backend: A dedicated API that runs the AI agents; reaches data only through the App backend.
• ELT: A data pipeline that processes our clients’ input files and loads the results into our DB through the App backend.

Client: React, React Router, TypeScript, Vite (SPA).

ELT: Python, dlt, dbt, cadquery / FreeCAD (CAD / STEP), Postgres, LLM and OCR document extraction, ML with Bruin and scikit-learn.

Infra: GCP Cloud Run, GCS, Cloudflare, CI/CD on GitHub.

You’ll work across all of it. On your deployments, you own the integration layer end to end and you’ll be the one telling us which parts of it deserve to become product.

Your main responsibilities

• Own a deployment end to end. From the first on-site workshop to a production system their sourcing decisions run on: scoping, integrations, data quality, rollout, adoption.
• Get their data in. Build the integrations and pipelines that pull from ERPs, PLMs, supplier portals and twenty years of spreadsheets, and make the output typed,