1. Build the substrate (this comes first)

• Take our internal knowledge-and-agent system from its current foundation to production: a versioned, structured knowledge base — markdown with YAML frontmatter, the pattern Google recently formalised as the Open Knowledge Format (OKF) — with retrieval that agents and people both read and trust.

• Design and ship agent workflows that do real work: ingestion, synthesis, drafting, plan stress-testing — on infrastructure we control.

• Architect for data ownership and vendor independence: self-hosted where it protects our IP, with an agent layer decoupled from any single LLM provider — so we can route across multiple models to cross-check and augment one another, swap or upgrade models transparently, and fall back to local models for cost or resilience.

2. Turn it into company velocity

• Hunt down AI use cases across R&D, operations, and administration — from quick wins to structural transformation — and prioritise ruthlessly by impact and feasibility.

• Get production workflows live where they compound: faster simulation and design iteration, less manual drudgery, higher-quality output.

• Build the internal craft — prompting, workflow design, agent orchestration — and the reusable patterns that let others build on what works.

3. Govern it properly (IP first)

• Own the data-governance posture for AI at Swave: what runs locally versus in the cloud, how partner-confidential and export-sensitive material is handled, and guardrails engineers can actually follow.

• Keep it lightweight and practical. We care far more about protecting our IP and our partners’ trust than about producing policy documents.

4. Bring the team with you

• Build a shared baseline of AI fluency and identify ambassadors in each team who carry it forward.

• You enable others to teach and adopt — you are not here to run a standing circuit of workshops.

A note on scope. There is also an AI dimension to our product and our research, owned by our product and algorithm teams. This role does not own that work — but you will collaborate with the people doing it, so the ability to hold a credible technical conversation with them matters.

This role reports to general management, a clear signal that this is a strategic priority for the company, not a peripheral function.

You operate across the entire organisation. You are equally at home in a room full of engineers and in a room with people who have never written a line of code.

We are looking for someone who thinks strategically and acts pragmatically — and does both with very little need to be the loudest voice in the room.

Must-have

• A real track record building and shipping AI in a live organisation — working systems people actually use, not slideware.

• Hands-on experience architecting AI agents and memory / retrieval systems. You understand RAG architectures in depth and you treat organisational knowledge as structured, version-controlled, agent-readable data — the philosophy behind emerging standards such as Google’s Open Knowledge Format, rather than reliance on any single specification.

• An architect’s mindset, with real computer-science and cloud-architecture literacy. You may never write a line of Python or TypeScript yourself — increasingly the job is to orchestrate AI to build — but you understand systems deeply: what a database is and when to reach for one, where an application lives, the trade-offs between cloud providers, how things like authentication and email deliverability actually work. You pair that with a product manager’s clarity about what to build and what to optimise for.

• A bias for model-agnostic, future-proof design. You think in abstraction layers that let us swap, combine, and upgrade models rather than hard-wiring to one vendor — and you default to owning our data.

• Fluency across many AI tools and workflows, with advanced prompting and workflow-design skill.

• A habit of staying at the frontier. This field moves weekly. You keep yourself genuinely current and translate what is new into what is useful for us.

• Proven effectiveness cross-functionally — equally credible with engineers and with non-technical stakeholders.

• The ability to set up projects from scratch and run independently.

• Strong communication: you make the complex feel accessible.

• An entrepreneurial mindset with a builder’s instinct.

Nice-to-have

• Experience deploying AI in a deep-tech, semiconductor, or industrial environment.

• Familiarity with self-hosted, local-model infrastructure (e.g. Ollama-class setups) and the trade-offs of running models on your own hardware.

• Experience with change management or organisational transformation.

We are a fabless semiconductor company headquartered in Leuven, Belgium, with a growing presence in the US. Backed by seasoned leaders and a team of passionate engineers, we’re pioneering holographic technology at the intersection of optics, semiconductors, and systems design.

At Swave, you’ll find:

• A culture built on authenticity, trust, and openness.

• A small but ambitious team with a bias for action.

• An environment where creativity and purpose drive everything we do.

• Space to take ownership, experiment, and see your ideas come to life.

We believe the best work happens when people feel valued and inspired. That’s why we offer a competitive salary package tailored to your skills, expertise, and impact.

At Swave, you’ll work at the cutting edge of optics and semiconductors, where every customer engagement drives adoption of technology that changes the world. We know life doesn’t fit neatly into a 9-to-5 schedule, so we put results before hours — with flexibility and remote options where it makes sense. Our headquarters in Leuven, a city alive with technology, culture, and creativity, provides the perfect backdrop for innovation and connection.

Most importantly, you’ll be part of an informal and supportive team where authenticity matters. Here, you can bring your true self to work, share your ideas openly, and celebrate successes together.

To set clear expectations, here is what success looks like in the first six months:

• The internal knowledge system is in regular use across teams.

• At least two production AI workflows are running where they matter (R&D, operations, or administration) and demonstrably saving time or lifting quality.

• A clear, lightweight data-governance posture is in place — one that engineers actually follow.

• A baseline of AI fluency exists across the teams, with named ambassadors carrying it forward.

Our headquarters are in the vibrant city of Leuven, Belgium. While the magic happens on-site, some hybrid work options are available.

Applicants must be currently authorised to work in Belgium on a full-time basis.
No visa sponsorship is available for this position.