Lead AI​/Machine Learning Engineer

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 results-oriented and hands-on AI/ML Engineer with a passion for Generative AI and LLMs, to design, build and deploy critical AI initiatives that drive value for the business. You will focus on driving complex projects from concept through delivery. You will be responsible for designing and implementing robust solutions, scalable AI and agentic solutions, writing production-quality code, and collaboration with cross-functional teams and stakeholders.

This position is in office.

• Architect, design, and develop AI applications, integrating with AWS Bedrock, Google Vertex AI, Microsoft Azure, and other LLM suites.
• Design, build, and deploy complex, scalable AI solutions, including multi-step agentic workflows and multi-agent systems.
• Develop and orchestrate AI agents capable of complex reasoning, planning, and dynamic tool use to solve business problems.
• Design and implement effective prompts, configure LLM settings, and optimize output through prompt crafting, context engineering, RAG, fine‑tuning, and other techniques.
• Design, implement, and manage robust evaluation strategies and frameworks specifically for Large Language Models (LLMs) and the agentic systems built upon them, assessing model quality, task completion reliability, safety, and effectiveness.
• Act as a hands‑on technical expert, guiding design decisions and ensuring adherence to best practices in AI development, LLMOps, testing, and deployment.
• Collaborate closely with product managers, data scientists, client teams, vendors, and other partners to define requirements, adapt plans, and ensure successful outcomes.
• Identify and mitigate technical risks and roadblocks impeding project delivery.
• Represent the technical aspects of your initiatives with senior leaders and partners.
• Contribute hands‑on to development and troubleshooting, especially on challenging technical problems, to ensure project momentum.
• Lead research and development efforts into emerging tools and technologies, with a particular focus on advancements in Generative AI, LLMs, and related technologies.
• May manage direct reports and/or lead junior team members, which include professional staff specializing in different technical disciplines and may also manage the work of further professional staff in a matrixed organization.

• 7 or more years of combined experience designing, building, and deploying AI/ML solutions, including 1-2 years of hands‑on experience with GenAI technologies.
• Experience with Retrieval‑Augmented Generation (RAG) architectures.
• Familiarity with Vector Databases (e.g., Milvus, Pinecone, Chroma
  
  DB).
• Expertise with AI application and agentic frameworks (e.g., Lang Chain, Lang Graph, Google ADK, Strands Agents, OpenAI Agents SDK, CrewAI, Llama Index).
• Experience with cloud platforms such as Google Vertex AI, AWS Bedrock, or Microsoft Azure.
• Strong understanding of data preprocessing techniques for LLMs, including tokenization, embedding, and feature engineering, to optimize model performance and accuracy.
• Proficiency in prompt engineering and context engineering techniques and approaches.
• Strong proficiency in core programming languages used in AI/ML (e.g., Python).
• Deep understanding of AI agent architectures, including concepts like planning, memory, and tool integration (e.g., ReAct).
• Solid understanding and practical experience applying MLOps…