Résumé du poste:

Job Requirements

BUILD SOMETHING BIGGER THAN YOURSELF

The Walt Disney Company, together with its subsidiaries and affiliates, is a leading diversified international family entertainment and media enterprise with the following business segments: Disney Entertainment, ESPN, Disney Parks, and Experiences and Products. From humble beginnings as a cartoon studio in the 1920s to its preeminent name in the entertainment industry today, Disney proudly continues its legacy of creating world-class stories and experiences for every member of the family. Disney’s stories, characters and experiences reach consumers and guests from every corner of the globe. With operations in more than 40 countries, our employees and cast members work together to create entertainment experiences that are both universally and locally cherished.

How you will contribute to our magical experience?

Disney Financial Insights (D-Fi) is a new platform reimagining how Finance teams and budget owners across Disney interact with financial data — replacing manual pulls, static reports, and email-driven approvals with one AI-powered, orchestrated experience built on top of Disney financial systems.

We're hiring the founding engineering team for this AI framework. One theme unifies every engineering role on D-Fi: autonomy and trust. You build the agents that act autonomously — designing how they execute finance processes end-to-end and knowing exactly when to hand off to a human instead. You'll own the agentic workflow layer: the core of D-Fi's autonomous finance capability. This means designing, building,



and maintaining the multi-agent orchestration framework that enables touchless execution of real finance business processes.

The Lead Machine Learning Engineer (Agentic Workflows) will be responsible for:

Own the agentic workflow layer. Design, build, and maintain the multi-agent orchestration framework that enables touchless execution of finance business processes — close cycle automation, variance analysis, forecast change routing, report generation, and anomaly response. Own agent architecture decisions. Determine how agents are structured, how they hand off to each other, how they handle failure modes and exceptions, and how human-in-the-loop escalation is triggered when autonomous action isn't appropriate. Consume and enforce delegated identity in agent execution. Ensure every agent action executes under the identity and entitlements of the user (or process) it's acting on behalf of — propagating that identity context correctly through every agent-to-agent handoff, and never allowing an agent to execute an action the underlying human wouldn't be authorized to take directly. Set the prompt and context engineering standard for the team. Lead the discipline of how LLMs are instructed, constrained, and evaluated across every agent on the platform, not just your own. Partner directly with finance domain experts. Map real finance processes into agentic workflows that can run with minimal human intervention,



translating process knowledge into system behavior. Design for graceful failure, not just success. Build exception handling and escalation paths so that when autonomous action isn't safe or appropriate, the system routes to a human cleanly and traceably.

What we need from you to be part of this story:

7+ years of software or ML engineering experience, with demonstrated ownership of complex systems end-to-end. Advanced level of English (must). Bachelor's degree in Computer Science, Engineering, or related field; advanced degree a plus but not required. Hands-on experience designing and shipping multi-agent or agentic LLM systems in production — not prototypes or demos. Strong judgment for failure-mode design: knowing where autonomy should stop and human review should start. Comfort setting technical standards other engineers follow, even without formal management authority. Genuine curiosity about finance processes — you don't need a finance background, but you'll need to learn how forecasting, variance analysis, and approval workflows actually work.

You'll be architecting, designing and writing production code.

AI/LLM:

Production experience building multi-agent systems — agent-to-agent handoff, task decomposition, and orchestration patterns. Strong prompt and context engineering skills, including designing constraints and guardrails for autonomous LLM-driven actions. Experience with agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or equivalent) and major model providers/APIs (Anthropic Claude, OpenAI, or equivalent). Understanding of evaluation methodolog

📌 Lead Machine Learning Engineer (Buenos Aires)
🏢 The Walt Disney Company (Corporate)
📍 Buenos Aires