Founding implementation strategist [ERP + AI]

TL;DR

• We're building the SAP/NetSuite of the AI era for mid-sized companies in the real-world economy, starting with their supply chain operations (buy, sell, stock, produce)

• Founding deployment strategist: you deploy two things into real factories, in the same project, the ERP that runs their operations, and the AI agents that work inside it. Every client goes from signed contract to go-live on the date we promised, running on Piston, with agents already doing part of the work

• A rare shape: someone who has led client projects that landed in industrial companies, who is genuinely AI-native, and who will write the deployment playbook for the team and our system integrator partners

• €3M Pre-seed (Top-tier VCs)

What this role is (and isn't)

Most ERP deployments are people moving data around for a year. Incumbents quote six figures for it, and the client gets the same software they had, just newer. Ours run in eight to twelve weeks and deliver two things at once: a new ERP that runs buying, selling, stock and production, and AI agents inside it that read the orders, recompute the production load, prepare the purchases, and ask a human only when it matters.

This is not a product or a sales role, you are a deployment strategist. In one project, you replace a company's ERP and you put AI agents to work inside it.

Why now

Since day one, the founders have run every deployment themselves. Clients are now coming in faster than we can handle, and we need someone to own deployments. You work directly with Eric, CEO, who also leads product. That puts you as close as it gets to what success means at Piston: the success of our clients.

What you'll do

• Deploy the ERP, with AI. The client's sales, purchasing, stock and production move onto Piston: data migrated from their old ERP and their Excel files, configuration built from their own documents, training, go-live. AI does the heavy lifting, you do the judgment.

• Put the first AI agents live, during the ERP deployment. You decide what each agent does on its own, what it hands to a human for approval, and you make sure it's still running three months later.

• Write the deployment playbook. Every go-live feeds a playbook that our next deployment hires and our system integrator partners will follow.

• Map where the client's time goes. On site, you find the work that eats people's days: re-keying orders, chasing suppliers, rebuilding the same Excel every month. That map is the backlog of the next agents.

• Teach humans to work with agents. The people you deploy with have never supervised software that acts on its own. You show them how to trust it, check it and correct it.

• Hold the date and the budget. You size each deployment before the contract and protect that budget.

• Use the tools engineers build for you. Our engineers build the tools that let you deploy on your own: AI-assisted data migration from the client's old ERP and Excel files, and a builder for screens and agents. You tell them what slows you down, they make it disappear.

• Agentify your own work. On top of the tools our engineers build, you create your own agents for the deployment itself: configuration checks, test scenarios, training material, status reports. Every manual step you repeat twice becomes an agent for the next deployment.

What you bring

You bring the energy to deploy AI at scale in industrial companies. You learn fast from discovery, and you're as comfortable with founders and leadership teams as with operators on the shop floor.

Must have

• Customer-facing project experience. Two years or more managing client projects in an industrial environment, typically in consulting (operations, supply chain, transformation). You've held deadlines, managed stakeholders, and delivered.

• AI-native, for real. You use LLMs every day, and you've built things with them: automations, agents, prompts that run in production (Claude, GPT, n8n, Make, scripts, whatever works). You know what an agent can do alone, where it breaks, and where a human must stay in the loop.

• Listening before configuring. You translate what people say into what the system and the agents must do, and you notice when the two don't match.

• At ease in front of clients: Plant directors, finance, shop-floor operators. You can hold a room, and say no in it.

• A full load, gladly. Around 150 project days a year, several deployments running at once.

• Fluent English and French, both required. Our first clients work in French, our team writes in English, and you may support our deployments in the United States.

Nice to have

• You've automated a real operations process end to end, with measurable hours saved

• You've already taken a complex B2B software live

• Comfortable with AI

• You've worked around manufacturing or food operations: lots, BOMs, production orders, campaigns

• Seed / Series A experience, where nobody's job description holds for long

Talent beats checklists. If you're intrigued, apply anyway.

How we'll measure success

• ERP go-lives on the date we promised

• Days spent against the plan we sold

• Agents still running three months after launch, and the hours of work they take off the client's hands

What you'll get

• World-class peers

• The first hire of our deployment team: you write the playbook that future hires and our system integrator partners will follow, and you grow with the team it builds

• A front-row seat on how AI agents change the way real companies operate

• Regular company offsite (2–4 times per year) in great locations

• Ideal equipment and setup

• Remote-first (France), about 20% of your time on client sites

Process

We value efficiency and clarity. The entire process takes no more than 2 weeks:

1. 30-minute intro call with Florian (CRO)

2. 60-minute deployment deep dive with Eric (CEO)

3. 30-min cultural-fit interview with Eugène (CTO)

4. 2 days paid assessment, on a real client situation.

5. Offer: If it's a match, we move fast.

Interested? Tell us about a client project you led, and something you automated with AI. A short, personal note goes a long way, AI-generated cover letters don't.

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