AI Deployment Strategist

The job

A client has decided agentic AI is the way forward. Budget approved, sponsor enthusiastic, (potential) use cases selected. Nothing runs yet. That's where you come in. You turn their ambition into a solution that works on their data, in their system, with their people, under their constraints.

The AI Deployment Strategist role is a combination of building and spending time with the people who’ll use what you build, watching how they work today and where they lose their time. That combination is what makes this role different from other engineering positions that own the deep technical work, and the platforms it runs on. You sit closer to the business: you advise on what gets built, you build the working version, and you care whether anyone ends up using it.

What you'll be doing

Decide what to build

• Sit with the people doing the work and map how it really happens, not how the documentation says it does

• Find the real bottleneck, and design the process you'd build today with agents in it from the start

• Pressure-test the use cases on the table: what's feasible, what the data allows, what it takes to build

• Challenge the brief: push back when the approved use case isn’t the one worth building, when it needs a process fix first, or when an agent is the wrong tool

• Raise the questions early that have a big impact later: data, privacy, compliance, ownership of the decision, etc.

Build it

• Build the solutions (RAG-based assistants, agentic workflows, automation, etc.) that connect to the client's data and systems

• Write code that runs in production, working with the client's architects and security people to get it live in their environment

• Decide where the agent acts alone and where a person signs off, and shape that handoff so people trust it

• Set up the evaluation and monitoring the client needs to trust it over time

Make it stick

• Train the people who'll use what you built and the engineers who'll maintain it

• Capture feedback from stakeholders on outcomes and iterate to keep improving the value

• Write the documentation the client’s team needs to keep it running and extend on what you built

Requirements

What we're looking for

We're looking for a capable engineer who wants more than build: figuring out what's worth building, and making sure it gets used. We hire for judgment and drive rather than a complete checklist, and we look for people who can create momentum from a thin brief.

• ~3 years building and shipping software or data systems that people depend on, with a preference for simple solutions and the ability to own a system end to end (Python is a plus)

• Hands-on experience taking LLM tools and systems past the demo, and a feel for where they work and where they're limited (if most of that mileage came from your own projects, side builds, and weekend experiments, that counts)

• A background that signals ownership: founder, first engineer, technical lead, or the person everyone came to when something had to work by Friday

• Quick to get up to speed in new organisations, with an eye for how the work is really done and how a solution lands: the exceptions, the workarounds, the people who quietly go around the system

• Comfortable bringing structure and momentum when nobody hands you clarity

• The instinct to ask why before what, and the nerve to say so when the answer doesn't hold

• Stakeholder management across technical and non-technical audiences, with enough grounding in data and AI to ask the right questions and challenge the answers

• Fluent English and Dutch and/or French

Nice to have:

• Range (industry, tools, problem domains)

• Breadth (front-end, cloud setup, integration glue, etc.)

• Familiarity with agentic toolkit (MCP, retrieval, knowledge graphs, evaluation, observability)

• Time spent close to customers, or between a product and its users (consulting, solutions engineering, early-stage product work)

The Offer

• An attractive salary with extralegal benefits, including:

  • Mobility budget or a company car with fuel/charging card

  • Hospitalization and group insurance

  • High-end laptop

  • Smartphone with subscription

  • Substantial amount of holidays

  • Meal vouchers

  • …

• Diverse and welcoming work environment where you’ll collaborate & unwind with colleagues from different cultures and disciplines. Organising both fun & professional events and initiatives is actively encouraged and supported.

• A training budget for individual and team learning opportunities.

• Tons ofteam-building events and sports initiatives to stay connected and unwind.

Our main offices are in Leuven and Ghent, with co-working spaces in Charleroi and Antwerp. These locations are always available for brainstorming sessions or team events. As a Dataroots expert, much of the work can be done remotely, but expect to be on-site with clients regularly. Ready for a new chapter as an AI Deployment Strategist at Dataroots? Great! Apply now!