Lead Product Software Architect — AI & Data

ph3Lead Product Software Architect — AI Data /h3 pYou will be part of the ‘Tech B.V.’ team, which focuses on development, service and delivery of technology solutions that support Twinfield’s digital products and services. /p h3Why this role exists /h3 pWe’re building AI-powered capabilities directly into customer-delivered software — not demos, not labs. This role owns the architecture that turns our data estate into an AI-ready product platform and makes AI features reliable, governable, secure, and scalable in production. You’ll lead the modernization of our product data estate (schemas, pipelines, contracts, governance, and access patterns) so we can ship AI/ML and GenAI capabilities quickly and safely. /p h3What you’ll own (outcomes) /h3 ul liA clear bAI + Data reference architecture /b that product teams can execute without heroics: from ingestion → curation → feature/embedding layers → serving → monitoring. /li liA modernized data estate that supports rapid iteration: bschema evolution /b, lineage, quality gates, and scalable access patterns (batch + real‑time/event‑driven where needed). /li liAI capabilities that are bproduction‑grade /b: measurable quality, observable, performant, fully automated deployments, governance, and cost‑optimized. /li /ul h3What you’ll do (Responsibilities) /h3 pb1) Architect AI‑enabled product capabilities (customer‑facing) /b /p ul liTranslate business goals and product requirements into bend‑to‑end architecture /b for AI features (e.g. predictive ML, recommendations, GenAI, agentic workflows). /li liDefine integration patterns between product services, data systems, and AI components (APIs, including MCP/A2A, ARG, events, model/agent serving, evaluation harnesses). /li liEvaluate NFR tradeoffs and ensure delivery adherence (e.g. latency, cost, security, resiliency, and maintainability). /li /ul pb2) Modernize the data estate to be AI‑ready /b /p ul liLead modernization of legacy data estates into a bgoverned, scalable architecture /b (lakehouse/data mesh patterns, curated layers, data products, and contracts). /li liDrive improvements in data quality, lineage, metadata, and discoverability — treat data pipelines as bsoftware /b (versioning, testing, CI/CD). /li liEstablish canonical models/semantic patterns that support analytics and AI/ML workloads (features/embeddings, training/serving parity). /li /ul pb3) Operationalize AI (MLOps/LLMOps) the “paved road” way /b /p ul liDefine standards and reusable patterns for: bfeature stores, model registries, experiment tracking, promotion workflows, drift monitoring, and retraining /b. /li liBuild reference implementations and enable teams to ship features repeatedly — moving from PoC to governed production delivery. /li liOwn architectural testing/validation practices for AI components: quality, robustness, security, and performance. /li /ul pb4) Make it safe: governance, privacy, security, compliance /b /p ul liEmbed responsible AI and governance controls into the lifecycle: auditability, transparency, bias/risk considerations, and secure‑by‑design patterns. /li liPartner with Security/Privacy/Legal to ensure our AI and data systems meet obligations without killing delivery velocity. /li /ul pb5) Lead through influence (engineering leadership) /b /p ul liAct as a technical leader and mentor: clarify direction, unblock teams, and raise the architecture/engineering bar through reviews, guidance, and coaching. /li liCommunicate complex tradeoffs clearly — influence product, engineering, and leadership stakeholders with pragmatic options and crisp decisions. /li /ul h3What you’ll bring (Minimum qualifications) /h3 ul lib8–12+ years /b building and evolving complex software products (SaaS/distributed systems required), including architectural leadership. /li liProven experience integrating bAI/ML or GenAI /b into customer‑facing software (not just internal analytics) — shipping to production with monitoring and operations. /li liHands‑on experience modernizing data estates: data modeling, integration, pipelines, lineage, and scalable storage/compute patterns. /li liExperience designing secure AI systems (threat modeling for prompt injection/data leakage, model supply chain controls, etc.). /li liStrong understanding of modern data architecture concepts: curated layers, governance, data products/contracts, and event‑driven/streaming where needed. /li liPractical DataOps/MLOps understanding: environments, CI/CD, promotion gates, drift detection, rollback/incident patterns, and operational monitoring. /li liAbility to write and maintain high‑quality architecture artifacts: blueprints, specs, ADRs, and reference implementations that teams actually use. /li /ul h3Nice‑to‑have (Strong differentiators) /h3 ul liExperience with blakehouse/data mesh /b transformations at scale and implementing strong governance/catalog patterns. /li /ul /p #J-18808-Ljbffr