1 week ago

AI Enablement Lead

MR MARVIS

Amsterdam

1 week ago

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AI Enablement Lead

MR MARVIS, an Amsterdam-based menswear brand and Certified B Corporation, is seeking an AI Enablement Lead to serve as the technical owner of AI across the company. The role involves hands-on building of AI systems, defining technical direction, leading stakeholders, ensuring reliability and governance, and driving company-wide AI adoption. Candidates must be AI-native with production experience, strong stakeholder management, and an analytical mindset.

HybridFull-timeEntry LevelAI SystemsAgents

AI Enablement Lead

Apply

HybridFull-timeEntry LevelAI Systems

Salary

Not specified

Work Location

Amsterdam, North Holland, Netherlands, NL

Work Model

Hybrid working with an inspiring office in the heart of Amsterdam

Employment Type

Full-time

Experience Level

Not Applicable

Core Qualifications

Technical (Must-have)

AI systemsAgentsEvaluationPrompt engineeringContext engineeringShopifySanityAlgoliaBigQueryData platformInternal APIsRetrieval pipelinesInternal copilotsInstrumentationMonitoring

Soft Skills

Stakeholder managementAnalytical mindsetPragmaticCommunicationLeadershipProblem solving

Preferred Qualifications

Technical (Nice-to-have)

E-commerceConsumer productSearch systemsRecommendation systemsPersonalization systemsExperimentation frameworksMeasurement frameworks

Key Responsibilities

•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, BigQuery, our data platform and internal APIs).
•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.
•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.
•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.
•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.

AIRetailFashionE-commerceAmsterdamFull-timeOn-siteHybridB CorpAI Enablement

Key Responsibilities

•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, BigQuery, our data platform and internal APIs).

•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.

•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.

•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.

•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.