Sr Machine Learning Engineer

Job Summary:

At Disney Experiences Technology our team creates world-class immersive digital experiences for the Companys premier vacation brands including Disneys 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-onSenior Machine Learning Engineerto design develop and deploy high-impact AI/ML solutions that drive measurable business value across our entertainment 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 Youll 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 12 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:

• Bachelors 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:

• Masters 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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