Director, AI Application Engineering

About Use Case Engineering

Use Case Engineering is the production delivery arm of Marriotts AI Engineering organization. We embed forward deployed AI engineers with teams across the enterprise carry the companys highest priority AI use cases from intake through production and turn each delivery into reusable patterns SDKs and reference architectures that make the next one faster.

The Role

As Director AI Application Engineering you own the engineering of AI applications and agent systems across a portfolio of enterprise use cases. You lead a group of forward deployed engineers set the architecture and stay hands on in the code. Your portfolio carries the hardest engagements: multi system integrations vendor engineering teams delivering alongside us and decisions that set precedent for the rest of the AI program. You work across build consult advise and monitor engagements at the same time and you are accountable both for what reaches production and for what happens to it afterward. Reports to the Senior Director AI Engineering.

Key Responsibilities

• Lead forward deployed engineers delivering AI applications and agents for a business vertical holding the technical bar from intake through production handoff

• Design and build agent systems tool and API integrations and retrieval pipelines on the enterprise AI platform stack using vendor SDKs and agent development kits

• Own architecture decisions for your portfolio and carry them through the enterprise governance gates including agent tier classification security engagement review and architecture review

• Direct vendor and systems integrator engineering teams delivering alongside your own setting the design and holding the quality line

• Stand up evaluation and monitoring for everything you ship so agent quality is measured rather than asserted

• Contribute working patterns SDK modules and reference implementations back to the shared library after every build and retire the ones that stop earning their place

• Build handoff packages including runbooks dashboards and documentation that let business and platform partners own solutions long term

• Mentor engineers across the function and set architectural guardrails others can follow without you in the room

Required Qualifications

• 8 years in software engineering including 3 years building AI or machine learning applications enterprise integrations or platform services; or 6 years with a Masters degree

• Production grade Python and/or TypeScript; Rust is a plus

• Hands on LLM application development: agent design tool calling prompt engineering retrieval and evaluation

• Working depth in the agent interoperability stack: MCP A2A Watson ADK and LangChain or comparable frameworks

• Technical leadership across several concurrent teams or deliveries

• Daily use of AI powered coding assistants as working engineering tools

• Experience directing vendor engineering teams or systems integrators

Preferred Qualifications

• Two or more enterprise AI platforms (AWS Azure or Vertex watsonx Writer Copilot Studio or comparable)

• Production retrieval systems: vector stores embedding models chunking and context strategy offline and online evaluation

• Multi agent orchestration frameworks or vendor native agent SDKs

• Reusable engineering assets such as SDKs libraries or reference architectures adopted by teams outside your own

• Enterprise governance and compliance workflows for AI including security privacy and model risk

• REST API integration design in a large enterprise and architecture documentation that stands up to a review board

• Hospitality travel or large scale consumer services