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 make autonomous decisions durable — turning models and agents built by others into reliable, monitored, production systems Disney can depend on every day. You'll own model productionalization, operational reliability, and the ML infrastructure layer — taking models and agents from development into production, keeping them current as Disney's data and processes evolve,



and making sure the platform meets Disney's security, compliance, and audit standards for AI handling financial data.

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

Own LLM fine-tuning and model customization. Build training datasets, run fine-tuning pipelines, and evaluate model performance on finance-specific tasks — variance commentary generation, natural language query interpretation, and document intelligence. Own the RAG architecture end-to-end. Design and maintain the vector database, embedding strategy, document ingestion pipeline, and retrieval quality that determine how well the platform answers finance questions. Design and maintain the context catalog. Build the structured knowledge base of Disney financial data, policy rules, org hierarchy, and process documentation that grounds every agent and LLM response across the platform. Ensure model outputs are accurate, grounded, and auditable. Build the evaluation frameworks that measure groundedness and accuracy continuously. Own document intelligence quality. Ensure unstructured and semi-structured finance documents are ingested, chunked, and retrieved in a way that produces reliable, traceable answers.

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

6-7+ years of ML engineering experience, with demonstrated depth in NLP,



retrieval systems, or applied LLM engineering. 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 fine-tuning foundation models for a specific domain, including building and curating training data. Track record of building and operating a production RAG system, not just experimenting with one. Comfort building rigorous evaluation frameworks for subjective, hard-to-measure outputs like generated commentary or natural language answers. Genuine curiosity about finance processes — you don't need a finance background, but grounding answers correctly requires learning the domain.

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

AI/LLM:

Production experience fine-tuning foundation models, including dataset construction, training pipeline design, and evaluation on domain-specific tasks. Deep working knowledge of RAG architecture — embedding models, chunking strategy, retrieval quality tuning, and re-ranking. Experience with vector databases/retrieval stores (e.g., Pinecone, Weaviate, pgvector, or Snowflake Cortex Search). Experience with major model providers and APIs (e.g., Anthropic Claude, OpenAI, or equivalent enterprise LLM platforms). Strong understanding of LLM evaluation methodology — groundedness/hallucination testing, human-in-the-loop feedback systems, and regression testing for prompt/model changes.

Data & Databases:

Strong data modeling background, including how to structure a context catalog or knowledge base for reliable retrieval.

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