Senior Director, AI Engineering Lead

Job Description Summary: 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   product s   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 Technology to translate business goals into AI capabilities, align technical direction, and ensure reusable AI capabilities are successfully adopted across the product portfolio   Champion AI engineering excellence:   promote best practices, reusable components, tooling, and engineering patterns that accelerate AI delivery across squads. Help upskill engineers and continuously build AI engineering capability across the organization   ​Requirements & Qualifications   BS or MS in Computer Science, Machine Learning, Engineering, or a related technical discipline, or equivalent practical experience   8-10+ years of experience in software or AI/ML engineering, including 3+ years leading or managing engineering teams, with   a track record   of shipping production systems that serve real users at scale   Demonstrated