Sr Machine Learning Engineer

Sr Machine Learning Engineer

At Disney Experiences Technology, our team creates world-class immersive digital experiences for the Company's premier vacation brands including Disney's Parks & Resorts worldwide, Disney Cruise Line, Aulani, A Disney Resort & Spa, and Disney Vacation Club. The Disney Experiences Technology team is responsible for the end-to-end digital and physical Guest experience for all technology & digital-led initiatives across the Attractions & Entertainment, Food & Beverage, Resorts & Transportation, and Merchandise lines of business as well as other initiatives including the MyDisneyExperience app and Hey, Disney!

The team is seeking a results-oriented and hands-on Senior Machine Learning Engineer to design, develop, and deploy high-impact AI/ML solutions that drive measurable business value across our entertainment company. In this role, you will lead complex, cross-functional projects with a strong emphasis on reuse, scalability, reliability, and performance.

The Senior ML Engineer will report to the ML Engineering Manager.

This position is in office.

The DXT AI Technology Platform team is responsible for building an AI enablement platform for the DX segment that provides streamlined AI & Generative AI capabilities for the segment to build solutions around and on top of. The Senior Machine Learning Engineer will design, develop, implement enterprise grade and robust AI/ML solutions, including agentic systems, multi-modal models, RAG, and Responsible AI applications.

What You'll Do:

• Own the operational backbone of the AI/ML platform — design and run the CI/CD pipelines, model/agent versioning, automated deployment, rollback, and release management that move systems from experimentation to production reliably and repeatably.
• Stand up comprehensive observability and monitoring infrastructure — model/agent performance, drift, data quality, latency, cost, and reliability — with alerting and automated remediation where possible.
• Develop production-scale AI systems, including multi-step agentic workflows and multi-agent orchestration platforms, and operationalize them end to end.
• Drive complete ownership of the AI/ML lifecycle — implementation, testing, deployment, and continuous operational monitoring — delivering projects on schedule and to specification.
• Design and implement Responsible AI frameworks — hallucination detection, safety guardrails, evaluation systems, and observability — to ensure model reliability, accuracy, and ethical deployment.
• Establish evaluation frameworks for Large Language Models and agent-based systems, measuring model quality, task success rates, safety compliance, and operational effectiveness.
• Champion LLMOps and MLOps best practices — infrastructure-as-code, reproducibility, automated testing, and environment parity — across the platform.
• Partner strategically with cross-functional stakeholders including product managers, data scientists, application teams, vendors, and partners to align on requirements, iterate on solutions, and deliver successful outcomes.
• Provide hands-on technical leadership, driving architectural decisions across AI development, LLMOps, quality assurance, and production deployment.
• Proactively identify and resolve technical blockers that could impact project timelines or deliverables.
• Communicate technical strategy and progress to executive leadership and key stakeholders with clarity and confidence.
• Engage directly in development and problem-solving on high-complexity technical challenges to maintain project velocity and quality.
• Drive innovation through research and experimentation with emerging AI technologies and frameworks, evaluating and integrating new capabilities that advance our platform.

Basic Qualifications:

• 5+ years of proven expertise in designing, building, and deploying AI/ML solutions at scale, with 1–2 years of production experience in Generative AI technologies.
• Comprehensive MLOps / LLMOps experience with hands-on implementation of CI/CD pipelines, model and agent monitoring, versioning, observability, and lifecycle management in production.
• Production deployment experience on major cloud platforms (AWS, Azure, or GCP) with a demonstrated ability to architect, scale, and operate cloud-native ML solutions.
• Expert-level programming proficiency in Python and AI/ML development ecosystems.
• Strong foundation in machine learning including statistical modeling, supervised and unsupervised learning algorithms.
• Advanced skills in prompt engineering with a deep understanding of optimization techniques and best practices for LLM interactions.
• Versatile ML skillset spanning traditional techniques (classification, regression, clustering) and cutting-edge deep learning approaches.
• Production-grade Generative AI experience deploying and maintaining LLMs and multi-modal models in live environments.
• Exceptional analytical capabilities with a track record of solving complex technical problems and thriving in ambiguous, rapidly-evolving situations.
• Outstanding communication and collaboration skills with the ability to translate complex technical concepts for diverse audiences and drive cross-functional alignment.
• Success partnering across organizational levels from individual contributors to senior leadership, building trust and delivering results.
• Proven ability to influence and lead in matrix organizations where collaboration and relationship-building are essential to achieving outcomes.

Preferred Qualifications:

• Experience with container orchestration and infrastructure-as-code (Docker, Kubernetes, Terraform) for ML workloads.
• Experience with vector databases and embedding technologies.

Required Education:

• Bachelor's degree in Computer Science, Machine Learning, Mathematical Sciences, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.

Preferred Education:

• Master's degree or Ph.D in Artificial Intelligence, Machine Learning, Mathematical Sciences, Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.