Lead AI/Machine Learning Engineer

Job Posting Title:

Lead AI/Machine Learning Engineer

Req ID:

10133050

Job Description:

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.

The Decision Science Transformation team within DDSI is seeking a results-oriented and hands-on AI/ML Engineering Manager to lead the design, development, and deployment of high-impact AI initiatives that unlock measurable business value. In this role, you will lead complex, cross-functional projects with a strong emphasis on reuse, scalability, reliability, and performance. You will design, develop, and implement robust, agentic AI/ML solutions. Success in this role also requires close collaboration with product, data science, and business stakeholders.

This position is in office.

What You’ll Do :

• 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.

• Own the end-to-end development lifecycle of AI models and applications – from implementation, rigorous testing, deployment, and operational monitoring – ensuring timely and successful completion.

• Write clean, maintainable, and production-ready code for AI/ML model training, evaluation, and serving.

• Collaborate closely with product managers, data scientists, client teams, vendors, and other partners to define requirements, adapt plans, and ensure successful outcomes.

• Act as a hands-on technical expert, guiding design decisions and ensuring adherence to best practices in AI development, LLMOps, testing, and deployment.

• 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.

• 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 advances in AI, and drive their adoption where appropriate.

• 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.

Basic Qualifications:

• 7 + 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 traditional ML algorithms and statistical modeling techniques.

• Proficiency in prompt 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).

• Expertise with the AI/ML ecosystem, including LLM application and agentic frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex), and supporting libraries (e.g., Pydantic, Streamlit).

• Solid understanding and practical experience applying MLOps principles and utilizing associated tools for model deployment, monitoring, and lifecycle management of both models and agents.

• Proven experience developing and deploying solutions on at least one major cloud platform (e.g., AWS, Azure, GCP).

• Experience using or eva