Lead Product Software Architect — AI & Data

Lead Product Software Architect — AI & Data role at Wolters Kluwer's Twinfield team, focusing on building AI-powered capabilities into customer-delivered software. Responsibilities include architecting AI-enabled product features, modernizing data estates, and operationalizing AI with MLOps/LLMOps. Requires 8–12+ years of experience in SaaS/distributed systems and AI/ML integration.

HybridFull-timeLeadAI/MLGenAI

Lead Product Software Architect — AI & Data

Lead Product Software Architect — AI & Data role at Wolters Kluwer's Twinfield team, focusing on building AI-powered capabilities into customer-delivered software. Responsibilities include architecting AI-enabled product features, modernizing data estates, and operationalizing AI with MLOps/LLMOps. Requires 8–12+ years of experience in SaaS/distributed systems and AI/ML integration.

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HybridFull-timeLeadAI/ML

Salary

Not specified

Work Location

Hoevelaken, Gelderland, Netherlands, NL

Work Model

Hybrid working model (up to 2 office days per week)

Experience Required

12 years

Employment Type

Full-time

Experience Level

8–12+ years building and evolving complex software products

Core Qualifications

Technical (Must-have)

AI/MLGenAIdata modelingintegrationpipelineslineagestorage/compute patternsDataOpsMLOpsLLMOps

Soft Skills

leadershipmentoringcommunicationinfluencecoaching

Preferred Qualifications

Technical (Nice-to-have)

lakehousedata meshgovernance/catalog patterns

Key Responsibilities

•Architect AI-enabled product capabilities (customer-facing)
•Translate business goals and product requirements into end-to-end architecture for AI features
•Define integration patterns between product services, data systems, and AI components
•Evaluate NFR tradeoffs and ensure delivery adherence
•Modernize the data estate to be AI-ready
•Lead modernization of legacy data estates into a governed, scalable architecture
•Drive improvements in data quality, lineage, metadata, and discoverability
•Establish canonical models/semantic patterns that support analytics and AI/ML workloads
•Operationalize AI (MLOps/LLMOps) the 'paved road' way
•Define standards and reusable patterns for: feature stores, model registries, experiment tracking, promotion workflows, drift monitoring, and retraining
•Build reference implementations and enable teams to ship features repeatedly
•Own architectural testing/validation practices for AI components
•Embed responsible AI and governance controls into the lifecycle
•Partner with Security/Privacy/Legal to ensure our AI and data systems meet obligations
•Act as a technical leader and mentor
•Communicate complex tradeoffs clearly

LeadProduct Software ArchitectAIDataSaaSDistributed SystemsMLOpsLLMOpsDataOpsSoftware Development