Sr. Manager, AI Engineering

Deckers Brands•Washington, DC

1d•$137,500 - $185,600•Remote

About The Position

At Deckers Brands, Together, Every Step is a promise kept that every employee can bring their authentic self, is valued and supported, as a whole person, at work and beyond. Together, Every Step is how we continue to deliver exceptional business results, experience an amazing place to work, and have a positive impact on the communities and world around us. The Role As Sr. Manager, AI Engineering, you will lead Deckers’ enterprise AI engineering capability, owning the platform that provides governed access to foundation models and reusable patterns for retrieval augmented generation, agents, and LLM applications. You’ll drive the delivery of AI applications into production, ensuring responsible AI controls and production readiness. This role is central to Deckers’ digital transformation, empowering teams with innovative AI solutions that shape the future of our business. We celebrate diversity--of your background, your experiences and your unique identity. We are committed to ensuring an inclusive and equitable workplace where all of our employees can Come as They Are. We believe that when we bring our different perspectives to work, we are truly Better Together.

Requirements

Bachelor’s degree required, preferably in Computer Science, Engineering, or a related technical field; Master’s degree preferred.
8–12 years of software, data, or machine learning engineering experience building production systems.
3–5+ years leading engineering teams in cloud-native environments.
3+ years delivering production AI or machine learning systems, including 2+ years building generative AI or large language model applications.
Demonstrated experience taking generative AI from proof of concept into governed production, including evaluation, guardrails, and monitoring.
Hands-on experience with retrieval augmented generation, agent frameworks, tool use, and orchestration patterns.
Experience with foundation model platforms such as AWS Bedrock, Azure OpenAI, or equivalent.
Deep understanding of large language model application architecture, including retrieval, context management, tool use, agent design, and orchestration.
Strong grasp of AI evaluation methods, safety and security concerns, and cost awareness for AI systems.
Strong leadership and people-management skills with a focus on coaching and developing talent.
Excellent problem-solving, analytical thinking, and decision-making skills.
Strong communication and influencing skills across technical and business stakeholders.
Comfortable working in a fast-paced, matrixed, and global environment.

Responsibilities

Own the enterprise AI platform, including governed access to foundation models, unified gateway, model routing, prompt and context management, and cost controls.
Define and maintain reusable AI engineering patterns for retrieval augmented generation, agents, orchestration, and LLM application architecture.
Lead the evaluation framework for AI systems, including offline evaluation suites, online quality monitoring, regression testing, and acceptance criteria for release.
Deliver AI applications into production across customer-facing and internal use cases, from proof of concept through governed deployment and ongoing operation.
Manage operational quality for production AI systems, including latency, availability, cost per interaction, quality regression detection, and support coverage.
Track the foundation model and AI tooling landscape, evaluate new capabilities, and manage migration as models and providers change.
Own the AI engineering roadmap and staffing plan, translating enterprise and brand priorities into a sequenced delivery plan.
Manage and mentor AI engineers, direct external AI delivery partners, and supply AI engineering capability into cross-functional teams.
Establish production readiness bar for AI systems, including guardrails, safety controls, human-in-the-loop design, fallback behavior, and incident response.
Implement responsible AI policy as engineering controls, including model risk documentation, evaluation evidence, output monitoring, and audit trails.
Partner with business, digital, data, ML engineering, enterprise architecture, security, privacy, and infrastructure teams to identify, scope, and prioritize AI use cases.