Implementation Engineer

Polysense; Oost-Vlaanderen; Sur place; EUR 52 000 - 70 000; Plein temps

Résumé du poste

Polysense is seeking an Implementation Engineer to deploy and configure computer vision systems on customer sites. You will write code, validate outputs, and own the end-to-end implementation in a real industrial environment.

You bring Python proficiency, a CS background, and an understanding of applied AI. You’ll work directly with customers to ensure go-live success and post-launch stability.

Qualifications

• Proficient in Python; writing it in the field as part of delivery.
• Solid technical foundation from CS or equivalent background.
• Working knowledge of AI and how it behaves in practice.
• Prior client-facing experience with a delivery mindset.

Responsabilités

• Technical deployment and system configuration at customer sites.
• Write/adapt code to fit customer setups in real environments.
• Validate outputs are correct and production-ready.
• Prepare site and coordinate go-live for day one readiness.
• Own end-to-end implementation lifecycle with customer alignment.
• Post-go-live diagnostics, fixes and field feedback collection.

Connaissances

Python

CS background

AI knowledge

Client-facing

Technical ownership

Field-ready

Clear communicator

Formation

CS degree or equivalent

Description du poste

Polysense builds computer vision systems that give food producers real-time insight into their production lines: catching defects, reducing waste and turning what's happening on the floor into decisions that actually matter. Customers like Agristo, Lotus Biscoff and La Lorraine are already running on it.

As an Implementation Engineer, you are the person who makes the system work at the customer site. You configure it, validate it, debug it and own it. End to end. This is a technical delivery role. The client relationship matters, but your primary job is to make the implementation succeed technically. You write code. You solve problems. You don't wait for someone else to fix it.

The implementation succeeds because you make it work: technically, reliably and in a way the customer can trust on day one and every day after.

What you’ll be doing

01. Technical deployment & system configuration

• Configure the Polysense system for each customer's specific production environment: camera positions, detection parameters, rejection thresholds and everything in between. This is hands-on technical work that requires you to understand both the system and the production line.
• Write and adapt code to make the system fit the customer's setup. You're not applying a template. You're solving a technical problem in a real industrial environment.
• Validate that the output is correct and genuinely useful. You don't hand off until you're confident the system performs as expected under production conditions.
• Prepare the site and coordinate go-live, making sure the technical environment is ready before day one.

Key point:

This is the core of the role. You are the technical person on the ground. The implementation goes live because you made it work.

• End-to-end implementation ownership
• Own the full implementation lifecycle: from initial scoping and site preparation to go-live and post-launch stability. You're accountable for the outcome, not just your slice of it.
• Align timelines and expectations with the customer and your Implementation Manager. You flag risks early, communicate clearly and keep things on track without needing to be managed.
• Train operators and supervisors on how to use the system effectively. You stay close until they're genuinely confident. Not just technically briefed.
• Work directly with customer teams on the floor. You can have the right conversation with a production manager just as easily as with a technical contact. Both matter.

Key point:

You own the implementation, not just a part of it. That means the technical work, the customer relationship during delivery and the outcome.

• Post-go-live: diagnostics, fixes & feedback
• Be the first to respond when something goes wrong after go-live. You reproduce the issue, diagnose it and fix it yourself. You don't ticket it and wait.
• Gather and structure diagnostic information when an issue is beyond your scope, and hand it off as a clear, well-scoped brief. The quality of that handoff is part of the job.
• Spot patterns across implementations: which configurations keep causing friction, which customer questions reveal a gap in the product and which workarounds you keep applying. You feed those observations back in a way that's actually useful: specific, grounded and actionable.

Key point:

You're the closest person to what customers experience in production. That perspective is valuable and you're expected to use it.

What You BringBackground & Experience

• Python proficiency: it's our primary language and you'll be writing it in the field, not just reading it
• A solid technical foundation: computer science, software engineering or an equivalent background where you've built and debugged real systems
• Working knowledge of AI: you understand how AI systems behave in practice and can apply that understanding when configuring and troubleshooting in the field
• Prior client-facing experience: you've worked directly with customers before and know how to hold that relationship while staying focused on delivery

Skills & Mindset

• Technical ownership, end to end: You don't stop at "it works in the lab." You stay until it works in production, under real conditions, for the people who have to use it every day.
• Comfortable in the field: Industrial environments don't faze you. You can switch between a technical deep-dive and a conversation with a floor manager without missing a beat.
• Sharp communicator, low noise: You say what needs to be said: clearly, early and without drama. When something's off, you flag it. When it's fixed, you close the loop.

Nice to have

• Hands-on experience with computer vision or other applied AI systems
• Experience at a startup or scale-up where things move fast and you've had to adapt
• Background in industrial, manufacturing or food production environments

What Success Looks Like

• Implementations go live on time and the system performs as expected from day one
• Customers are actively using the system and getting real value from it after go-live
• Issues after go-live get resolved quickly and thoroughly by you, without escalating every problem
• Your configuration approach gets more reliable and efficient with each implementation
• The feedback you bring from the field is specific, grounded and actually changes how the team thinks about the product
• Customers see you as the technical expert they can trust. Not just during delivery but after.