AI Enablement Lead

About MR MARVIS
MR MARVIS is an Amsterdam-based menswear brand, dedicated to creating the perfect shorts, trousers and tops. As a Certified B Corporation, we’re proud to be part of a community of companies working together for change, and taking part in collective action to advocate for a shared goal of a global economy that benefits people and the planet.

What started in 2016 with three founders has now grown into a team of 300+ people representing 40+ nationalities. Having established a strong online presence across Europe, we’re now growing our international retail network. Today, you’ll find us in cities such as Amsterdam, Berlin, Paris and London.

We make slow fashion, with timeless design and craftsmanship at the centre. We are dedicated to doing better every day, creating value for our team, our suppliers, our customers, and everyone who interacts with our business.

What you’ll own
MR MARVIS is building an AI-native commerce and operating layer across the company, AI-driven discovery (GEO), customer-facing assistants (fit/size, delivery promise), decision intelligence for commercial teams, and the governance and reliability needed to scale it safely.

This role is the technical owner of AI at MR MARVIS. You report directly to the Tech Director and work as a close counterpart: you turn AI ambition into prioritized backlog based on ROI, working systems, and you make sure they are reliable, measurable, and adopted.

Technical direction for AI

Make architecture and build-vs-buy calls together with the Tech Director; evaluate models, vendors, and tooling on evidence rather than hype.

Define how AI systems integrate with our stack (Shopify, Sanity, Algolia, Big Query, our data platform and internal APIs).

Hands-on building

Prototype fast and in the open:
PoCs, agents, retrieval pipelines, evaluations, internal copilots. You write code and ship working things.

Take initiatives from prototype to production with Engineering and Data — instrumentation, evaluation, monitoring, rollback paths.

Establish a repeatable path from PoC → production, including data readiness checks, risk assessment, evaluation, and rollout.

Stakeholder leadership

Act as the single point of contact for AI across Commerce, Customer, Data, Ops, Growth, CX and People: surface the real problems, say no to the wrong ones, and sequence the rest.

Run discovery with non-technical stakeholders and translate in both directions — business outcome to technical design, and back again.

Report progress crisply to leadership through clear narratives, demos, and KPI tracking.

Reliability & governance

Own quality standards for AI in production: offline and online evaluation, quality thresholds, monitoring, incident playbooks.

Keep us safe and compliant: data classification, PII minimization, model/tool risk assessment and vendor evaluation, in partnership with Security/DPO.

No hallucinated delivery, returns, or fit promises to customers — ever.

Company-wide AI enablement

Raise the AI baseline of the whole company with the Tech Director and other engineers: education, workshops, hackathons, ambassadors, a central AI channel, and practical guardrails people actually use.

Expand AI coverage across departments through integrations and knowledge base grounding.

Outcomes we expect (first 6–12 months)

A clear, prioritized AI roadmap tied to company objectives, with an operating model behind it (intake, prioritization, visibility, ownership).

At least 2–4 AI initiatives live in production — not pilots — with adoption and KPI tracking.

A repeatable, documented playbook for taking AI work from PoC to production.

A working evaluation and monitoring foundation, so we can tell whether our AI is actually good.

Being on top of GEO implementation and follow the new…