# Lead Product Software Architect — AI & Data
## Why this role exists
You'll own the architecture that turns the data estate into an AI-ready product platform and makes AI features reliable, governable, secure, and scalable in production. Lead the modernization of the product data estate (schemas, pipelines, contracts, governance, and access patterns) to enable shipping of AI/ML and GenAI capabilities quickly and safely.
## What you'll own
- A clear AI + Data reference architecture that product teams can execute: from ingestion → curation → feature/embedding layers → serving → monitoring
- A modernized data estate supporting rapid iteration: schema evolution, lineage, quality gates, and scalable access patterns (batch + real-time/event-driven)
- AI capabilities that are production-grade: measurable quality, observable, performant, fully automated deployments, governance, and cost-optimized
## Key Responsibilities
1) Architect AI-enabled product capabilities
- Translate business goals and product requirements into end-to-end architecture for AI features (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 (latency, cost, security, resiliency, maintainability)
2) Modernize the data estate to be AI-ready
- Lead modernization of legacy data estates into governed, scalable architecture (lakehouse/data mesh patterns, curated layers, data products, contracts)
- Drive improvements in data quality, lineage, metadata, and discoverability — treat data pipelines as software (versioning, testing, CI/CD)
- Establish canonical models/semantic patterns supporting analytics and AI/ML workloads (features/embeddings, training/serving parity)
3) Operationalize AI (MLOps/LLMOps)
- Define standards and reusable patterns for: feature stores, model registries, experiment tracking, promotion workflows, drift monitoring, and retraining
- Build reference implementations enabling 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, secure-by-design patterns
- Partner with Security/Privacy/Legal to ensure AI and data systems meet obligations without killing delivery velocity
5) Lead through influence (engineering leadership)
- Act as technical leader and mentor: clarify direction, unblock teams, 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
## Minimum Qualifications
- 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)
- Strong understanding of modern data architecture concepts: curated layers, governance, data products/contracts, event-driven/streaming
- Practical DataOps/MLOps understanding: environments, CI/CD, promotion gates, drift detection, rollback/incident patterns, operational monitoring
- Ability to write and maintain high-quality architecture artifacts: blueprints, specs, ADRs, and reference implementations
## Nice-to-have
- Experience with lakehouse/data mesh transformations at scale and implementing strong governance/catalog patterns
## Benefits
- 36–40 hour working week with flexible working hours
- Hybrid working model (up to 2 office days per week)
- Competitive salary aligned with senior-level responsibility
- 25 vacation days (based on 40 hours)
- 50% pension contribution reimbursed by Wolters Kluwer
- Informal, collaborative working environment with regular events
- Daily lunch buffet, fresh fruit, and great coffee