Lead AI Products Architect

Job Summary

The Lead AI Products Architect is a senior technical leader within the AI Products Management Department who collaborates with Product Managers, AI Research, and Delivery teams to ensure AI product designs are scalable, secure, and production-ready. This role oversees the technical quality across the AI product lifecycle, focusing on architecture, model performance, data readiness, and operational excellence.

Responsibilities

• Manage the complete technical architecture for AI products including GenAI, RAG, Agents, and ML components such as inference services, orchestration, data pipelines, APIs, and integrations.
• Establish reference architectures and reusable components like RAG modules, evaluation frameworks, prompt/versioning systems, feature stores, vector DB patterns, and guardrails.
• Lead design reviews with Engineering, Platform, and Security teams to ensure solutions are scalable, resilient, cost-effective, and maintainable.
• Define success metrics and acceptance criteria for AI models including accuracy, groundedness, latency, cost, safety, robustness, and drift.
• Develop offline and online evaluation methods such as A/B testing and maintain the AI evaluation playbook covering hallucination detection, retrieval quality, safety, jailbreak resistance, and ML metrics like precision/recall and calibration.
• Own the AI product data strategy addressing sources, contracts, quality checks, lineage, governance, and privacy-by-design, and define data readiness gates for PoC, MVP, and Scale phases.
• Collaborate with Data Engineering on pipelines and continuous data quality monitoring.
• Specify MLOps/LLMOps requirements including model registry, CI/CD, prompt/config versioning, observability, monitoring, and rollback strategies, and define runtime controls such as guardrails, policy enforcement, red-teaming, rate limiting, and audit logging.
• Provide technical leadership by translating product requirements into system constraints and non-functional requirements, identifying risks early, proposing trade-offs, and mentoring Product Managers on AI technical fundamentals.

Requirements

• Experience in designing end-to-end AI product architectures including GenAI, RAG, Agents, and ML systems.
• Ability to define and implement model performance metrics and evaluation strategies.
• Knowledge of data strategy, governance, and privacy considerations for AI products.
• Familiarity with MLOps/LLMOps practices including CI/CD, monitoring, and rollback mechanisms.
• Strong skills in technical leadership and collaboration with cross-functional teams.
• Capability to mentor Product Managers on AI technical concepts.

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