Job description
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 valueand 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
37 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 its missing.
Excellent communication skillsable 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)
All done!
Your application has been successfully submitted!
Required Experience:
IC
Job descriptionJob Title: AI Product Specialist Databricks DeploymentLocation: (Hybrid) RotterdamEngagement: Full-time 5 days a weekContract duration: 3-6 monthsStart date: ASAPAssignment OverviewWe are looking for an interim AI Product Specialist to support clients with the rapid deployment and sc...
Job description
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 valueand 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
37 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 its missing.
Excellent communication skillsable 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)
All done!
Your application has been successfully submitted!
Required Experience:
IC
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