# 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