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# Lead Product Software Architect - AI & Data

- **Company:** Wolters Kluwer N.V.
- **Location:** Hoevelaken, Netherlands
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Software as a Service, Continuous Integration, Data Architecture, Information Leak Prevention, Data Systems, Software Design Patterns, Distributed Systems, Metadata, Software Product Management, DataOps, Systems Integration, Management of Software Versions, Deployment Automation, Data Analytics, Machine Learning Operations, Drift Detection, Data Pipelines, Agent2Agent Protocol
- **Published:** October 29, 2026
- **Apply:** https://wk.wd3.myworkdayjobs.com/External/job/NLD---Hoevelaken/Lead-Product-Software-Architect---AI---Data_R0055258

## About the Role

* 8-12+ years building and evolving complex software products (SaaS/distributed systems required), including architectural leadership.
 * Proven experience integrating AI/ML or GenAI into customer-facing software (not just internal analytics) - shipping to production with monitoring and operations.
 * Hands-on experience modernizing data estates: data modeling, integration, pipelines, lineage, and scalable storage/compute patterns.
 * Experience designing secure AI systems (threat modeling for prompt injection/data leakage, model supply chain controls, etc.).
 * Strong understanding of modern data architecture concepts: curated layers, governance, data products/contracts, and event-driven/streaming where needed.
 * Practical DataOps/MLOps understanding: environments, CI/CD, promotion gates, drift detection, rollback/incident patterns, and operational monitoring.
 * Ability to write and maintain high-quality architecture artifacts: blueprints, specs, ADRs, and reference implementations that teams actually use.

Nice-to-have (Strong differentiators)

 * Experience with lakehouse/data mesh transformations at scale and implementing strong governance/catalog patterns.

## Description

* A clear AI + Data reference architecture that product teams can execute without heroics: from ingestion * curation * feature/embedding layers * serving * monitoring.
 * A modernized data estate that supports rapid iteration: schema evolution, lineage, quality gates, and scalable access patterns (batch + real-time/event-driven where needed).
 * AI capabilities that are production-grade: measurable quality, observable, performant, fully automated deployments, governance, and cost-optimized.

What you'll do (Responsibilities)

1) Architect AI-enabled product capabilities (customer-facing)

 * Translate business goals and product requirements into end-to-end architecture for AI features (e.g. predictive ML, recommendations, GenAI, agentic workflows).
 * Define integration patterns between product services, data systems, and AI components (APIs, including MCP/A2A, ARG, events, model/agent serving, evaluation harnesses).
 * Evaluate NFR tradeoffs and ensure delivery adherence (e.g. latency, cost, security, resiliency, and maintainability).

2) Modernize the data estate to be AI-ready

 * Lead modernization of legacy data estates into a governed, scalable architecture (lakehouse/data mesh patterns, curated layers, data products, and contracts).
 * Drive improvements in data quality, lineage, metadata, and discoverability - treat data pipelines as software (versioning, testing, CI/CD).
 * Establish canonical models/semantic patterns that support analytics and AI/ML workloads (features/embeddings, training/serving parity).

3) 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 - moving from PoC to governed production delivery.
 * Own architectural testing/validation practices for AI components: quality, robustness, security, and performance.

4) Make it safe: governance, privacy, security, compliance

 * Embed responsible AI and governance controls into the lifecycle: auditability, transparency, bias/risk considerations, and secure-by-design patterns.
 * Partner with Security/Privacy/Legal to ensure our AI and data systems meet obligations without killing delivery velocity.

5) Lead through influence (engineering leadership)

 * Act as a technical leader and mentor: clarify direction, unblock teams, and raise the architecture/engineering bar through reviews, guidance, and coaching.
 * Communicate complex tradeoffs clearly - influence product, engineering, and leadership stakeholders with pragmatic options and crisp decisions.