AI-ML Product Engineering Manager

The Disney Decision Science + Integration (DDSI) is a consulting team that supports clients across The Walt Disney Company, including Disney Experiences (Parks & Resorts worldwide, Cruise Line, Consumer Products, etc.), Disney Entertainment (ABC, The Walt Disney Studios, Disney Theatrical, Disney Streaming Services, etc.), ESPN, and Corporate Finance. Key partners to the DDSI organization include Marketing, Finance, Business Development, Research, and Operations. We develop, analyze, and execute strategies and improve the value proposition for our Guests, Cast Members, and Shareholders. The team leverages technology, data analytics, optimization, statistical and econometric modeling to explore opportunities, shape business decisions and drive business value. 

Our team within DDSI is seeking a highly experienced AI Product Engineering Manager to lead solutions in the generative AI and AI/ML space. You will be responsible for designing the solution blueprints that bridge the gap between complex business problems and cutting-edge AI capabilities. This role is pivotal in translating product vision into a feasible and scalable technical architecture and with reusable design patterns, with a focus on designing the agentic workflows and service patterns that will commercialize DDSI's unique forecasting and optimization models for the enterprise AI Ecosystem.

Responsibilities:

• Design and own the solution architecture for DDSI's AI/ML solutions, including multi-step agentic workflows, RAG patterns for structured and unstructured data, and MCP-based "smart specialist" services.

• Partner with Product Managers and business stakeholders to translate complex business needs into viable, scalable, and secure technical solution designs.

• Serve as the primary technical liaison between DDSI and the central AI Factory engineering teams, ensuring our solutions are architected for seamless integration and performance on the enterprise platform.

• Lead the technical evaluation of new AI frameworks, vendor platforms, and architectural patterns, conducting rapid proof-of-concept builds to prove feasibility and guide technical direction.

• Act as a hands-on technical expert and mentor, guiding engineering teams on best practices for building robust, reusable, and maintainable agentic systems.

• Create and maintain architectural documentation, including reference architectures and "cookbooks" that enable other teams to leverage DDSI's patterns and services.

• Identify and mitigate technical risks in the solution design phase, ensuring long-term scalability and alignment with enterprise technology standards.

Basic Qualifications:

• 7+ years of experience in software engineering and/or data science, with at least 4+ years in a technical leadership or architecture role focused on AI/ML.

• Deep understanding of AI agent architectures, including concepts like planning, memory, and tool integration (e.g., ReAct, RAG, MCP).

• Expertise with the AI/ML ecosystem, including LLM application and agentic frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel).

• Proven full-stack experience designing scalable, services-oriented architectures on a major cloud platform (e.g., AWS, GCP).

• Strong proficiency in core programming languages used in AI/ML (e.g., Python).

• Excellent analytical and problem-solving skills, with the ability to navigate ambiguity and design elegant solutions for complex technical challenges.

Preferred Qualifications:

• Deep familiarity with Decision Science disciplines like optimization, simulation, and advanced forecasting.

• Experience designing solutions for multi-tenant platforms or two-sided marketplaces.

• Hands-on experience with enterprise MLOps/LLMOps tools and principles.

• Experience with knowledge graphs and semantic technologies.

• Demonstrated ability to effectively communicate complex architectural concepts to both technical and non-technical leadership.

• Familiarity with Disney business domain knowledge.

Education:

• Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related quantitative field.