Sr Machine Learning Engineer

Job Summary: 

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 Senior 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. 

About The Role & Team: 

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. 

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