AI Product Specialist – Databricks Deployment Interim

Job Title: AI Product Specialist – Databricks Deployment
Location: (Hybrid) Rotterdam
Engagement: Full-time, 5 days a week

Contract duration: 3-6 months

Start date: ASAP

We are looking for an interim AI Product Specialist to support clients with the rapid deployment and scaling of AI products on Databricks
. This is a hands‑on, delivery‑focused role for someone who can bridge data engineering, applied ML/analytics, and business value
—and who is comfortable moving quickly from discovery to production.

You will work with cross‑functional teams to ensure AI products are deployable, governable, and measurable, while keeping stakeholders aligned on outcomes, risks, and ROI.

• Deploy and operationalize AI products on Databricks, working closely with data engineers, data scientists, and platform teams.
• Translate business goals into deployable product requirements, including data needs, success metrics, and rollout approach.
• Design and optimize pipelines and environments (lakehouse patterns, compute, orchestration) to support analytics and ML workflows.
• Drive the path from PoC to production, including implementation planning, technical decision‑making, and delivery governance.
• Implement data governance and quality practices (data lineage, access, quality checks, compliance considerations) to meet enterprise standards.
• Act as a Databricks SME: advise on architecture choices, integration patterns, performance considerations, and operational readiness.
• Support value realization: define KPI/ROI frameworks with stakeholders and help communicate impact and adoption progress.

• 3–7 years in data product delivery, analytics engineering, applied AI, or similar roles (consulting experience is a plus).
• Proven experience with Databricks in real delivery contexts (platform capabilities, deployment patterns, production considerations).
• Strong understanding of data lifecycle and architecture (pipelines, lakehouse concepts, quality, governance).
• Strong stakeholder management: able to align technical and business teams and keep delivery outcome‑focused.
• Comfortable working in fast‑moving environments, creating clarity and structure where it’s missing.
• Excellent communication skills—able to explain trade‑offs, risks, and value in plain language.

• Experience with ML lifecycle/ML Ops concepts (model deployment, monitoring, retraining triggers, feature management).
• Familiarity with cloud platforms (Azure/AWS/GCP) and enterprise security/compliance constraints.
• Experience setting up or improving operating models for data/AI product delivery (ways of working, handovers, run/support).

• Deployment‑ready AI product plan (scope, architecture choices, success metrics, rollout approach)
• Working Databricks implementation (pipelines/environments) supporting analytics/ML workloads
• Governance + quality controls aligned with client standards
• Stakeholder‑ready progress and value reporting (KPI/ROI)