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

Assignment Overview

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.

Key Responsibilities

• 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.

Required experience & Skills

• 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.

Nice to have

• 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).

Deliverables (examples)

• 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)