Sr. Engineering Manager, AI and Agents

What You Will Do

• Lead, hire, develop, and scale AI engineering teams and technical leaders supporting multiple use-case pods and shared platform capabilities  (e.g., marketing creative generation, AI product experiences, agentic distribution, enterprise workflow automation), setting clear expectations, providing ongoing coaching and feedback, managing performance, career growth, and leadership development.
• Own delivery and engineering quality for LLM-based systems, agents, and integrations, from architecture and build through production operation, cost, latency, and reliability, ensuring teams make sound technical tradeoffs and deliver scalable, maintainable solutions across multiple products and services.
• Guide technical direction and lead the development and operation of the CoE’s shared AI platform: evaluation pipelines, behavioral and quality testing, prompt and model management, observability, DLP / sensitive-prompt guardrails, agent frameworks, and reusable AI capabilities that can scale across teams.
• Partner with Product, Architecture, and business leaders to translate product and business priorities into technical plans, prioritize engineering work, and balance capacity and resources across teams and initiatives; make informed tradeoffs across delivery commitments and technical investments, including build/buy/partner, vendor, and model decisions.
• Establish and maintain effective engineering practices across planning, design and code reviews, on-call, and incident management; proactively manage cross-team dependencies, operational risks, and systemic technical issues, and keep senior stakeholders aligned on key progress, risks, tradeoffs, and decisions.
• Ensure every launch meets CoE evaluation, security, and governance standards; collaborate with Legal, Compliance, and Security to move fast with the right guardrails and drive consistent adoption of these practices across AI engineering teams.
• Develop, scale, and champion  engineering standards, reusable patterns, and playbooks that enable the CoE and partner teams to adopt AI safely and quickly; establish knowledge-sharing practices that scale across teams.
• Stay close to the technology through critical architecture, design, and technical decisions, while maintaining primary accountability for engineering leadership and delivery through teams and leaders.
• Navigate complex, ambiguous, cross-cutting engineering challenges that span teams and organizational boundaries, establishing scalable approaches and driving alignment where precedent or clear solutions may not exist.

…