Architect and Deploy AI-Powered Media Solutions
Design and build production-grade AI systems that integrate with Disney's existing broadcast infrastructure, including media asset management (MAM) systems, master control, and third-party broadcast tools (e.g., Evertz, Skyline DataMiner)
Develop intelligent workflow automation solutions that bridge legacy broadcast systems with modern AI capabilities, creating seamless data flow between disparate platforms
Build and deploy APIs and microservices that expose AI capabilities to production teams, enabling real-time insights and decision support during live operations
Leverage large language models through APIs (OpenAI, Anthropic, Vertex AI, AWS Bedrock) and local models to create transformative solutions for content metadata generation, workflow optimization, and operational intelligence
Design and implement sophisticated prompt engineering strategies and orchestration frameworks (LangChain, Model Context Protocol) to build reliable, production-ready AI applications
Build and maintain robust MLOps pipelines for model deployment, monitoring, and iteration in cloud environments (AWS/GCP)
Implement containerized AI services using Docker and Kubernetes, ensuring high availability and scalability for production broadcast systems
Establish best practices for model versioning, performance monitoring, and operational reliability
Partner directly with broadcast engineers, media operations teams, and cross-functional partners to understand workflow challenges and translate them into technical requirements
Communicate AI capabilities and limitations clearly to non-technical stakeholders, building trust and understanding across the organization
Prototype and demonstrate new AI capabilities to stakeholders, gathering feedback and iterating rapidly

Requirements

7+ years of professional software engineering experience, with demonstrated focus on applied AI/ML solutions
Proven track record of independently designing, building, and deploying software systems that integrate with existing enterprise infrastructure
Experience working with APIs, databases, and third-party enterprise software systems
Bachelor's degree in Computer Science, Software Engineering, or related technical field, or equivalent practical experience
Familiarity with media asset management (MAM) systems and broadcast workflow tools
Excellent communication skills with the ability to collaborate effectively with both technical and non-technical stakeholders
Experience with: Python for production software development
Building with major LLM APIs (Google Vertex AI, AWS Bedrock, Anthropic, OpenAI) and orchestration frameworks (LangChain, Machine Context Protocol)
Prompt engineering skills and demonstrated ability to design reliable, production-ready AI applications
MLOps practices, including model deployment, monitoring, and CI/CD pipelines
Containerization (Docker) and orchestration technologies (Kubernetes)
Cloud infrastructure platforms (AWS or GCP), including deploying and managing AI services in production environments
Infrastructure-as-Code tools (Terraform, CloudFormation)
Video codecs, streaming protocols, or media standards (e.g., SMPTE)

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonAPIsMLOpsPrompt engineeringContainerizationOrchestration frameworksModel deploymentMonitoringCI/CD pipelinesMedia asset management

Soft Skills

CommunicationCollaborationProblem-solvingFeedback gatheringTrust building

Certifications

Bachelor's degree in Computer ScienceBachelor's degree in Software Engineering