Senior Director, AI Engineering Lead

Role Overview As part of the Product & Engineering team within Global Digital Network, the Senior Director, AI Engineering Lead will play a pivotal role in shaping how artificial intelligence is engineered, scaled, governed, and adopted across our key digital products portfolio. This role combines strategic technology leadership, organizational capability building, and deep technical expertise to accelerate the delivery of secure, scalable, and business-impacting artificial intelligence solutions. Reporting to the Head of Product Engineering and partnering closely with Product, Data Science, Technical Leads, and the Engineering Excellence Lead, you will define the AI engineering strategy, lead a team of AI Engineers, and establish the architectures, platform requirements, and reusable capabilities that enable AI at enterprise scale. You will ensure AI solutions are secure, scalable, production-ready, and seamlessly integrated into our product ecosystem, while shaping engineering standards, tooling, and best practices that accelerate AI adoption across the organization. The ideal candidate is a hands-on technical leader who combines deep AI engineering expertise with the ability to build teams and scale engineering capability. You have successfully designed, built, and operated production AI systems, evolved engineering practices based on rapidly changing AI capabilities, and coached engineers to deliver high-quality AI solutions. You balance innovation with pragmatism, making thoughtful trade-offs between speed, cost, reliability, safety, and maintainability. What You'll Do for Us Define the enterprise AI engineering roadmap: partner with Core Technology and Engineering teams to shape the evolution of AI platforms, orchestration and memory capabilities, developer tooling, reusable engineering services, and emerging AI frameworks that accelerate enterprise AI adoption Build and lead the AI Engineering team: build, lead, and develop a shared team of AI Engineers supporting products across the portfolio. Grow the organization's AI engineering capability through hiring, coaching, technical mentorship, and career development. Foster a culture of engineering excellence, experimentation, and continuous learning Lead the organization's most complex AI engineering challenges: operate as a player-coach by providing technical leadership on the organization's most complex AI initiatives. Partner with Tech Leads and engineering teams on model selection, prompt and agent architectures, retrieval and training pipelines, evaluation strategies, and other critical AI engineering decisions. Selectively contribute to the implementation of high-impact AI capabilities Define enterprise AI architecture and interoperability patterns: establish reference architectures and reusable engineering patterns for semantic layers, knowledge graphs, context engineering, Retrieval-Augmented Generation (RAG), GraphRAG, multi-agent systems, agent communication, tool orchestration, memory strategies, and secure interoperability using Model Context Protocol (MCP), Agent-to-Agent (A2A), and emerging enterprise integration standards Advance reusable AI engineering capabilities: develop reusable SDKs, templates, CI/CD patterns, testing frameworks, and engineering accelerators that enable product teams to build AI solutions consistently. Partner with Core Technology to ensure the underlying AI platform and orchestration capabilities support reliable and scalable enterprise deployment Define AI engineering operating patterns: establish enterprise patterns for prompt lifecycle management, evaluation pipelines, observability, experimentation, cost optimization, deployment, and continuous improvement of AI agents in production. Define AI-specific deployment patterns and operational requirements that enable product teams to ship AI safely at scale Establish AI observability and operational excellence: define enterprise-wide telemetry, tracing, runtime monitoring, reasoning diagnostics, token consumption analytics, operational dashboards, and evaluation frameworks for AI agents and LLM-powered applications. Continuously improve model quality, latency, token efficiency, runtime cost, observability, and business outcomes through experimentation and engineering optimization Embed responsible and governed AI: partner with governance leads to implement evaluation, guardrails, monitoring, access controls, audit logging, human-in-the-loop workflows, and secure agent execution so AI products are safe, explainable, compliant, and trusted by business users Advance digital twin capabilities: partner with Product, Data, and Core Technology teams to establish AI architectures and reusable capabilities that enable enterprise digital twin solutions across commercial and operational domains Partner across product and engineering: work shoulder to shoulder with Product Managers, Product Owners, Tech Leads, the Engineering Excellence Lead, Data Science, Design, and Core Tec