Sr. Staff, Data Science & Applied AI

Welcome to Warner Bros. Discovery... the stuff dreams are made of.

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When we say, "the stuff dreams are made of," we're not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD's vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what's next...

From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive.

As Sr. Staff - Data Science & Applied AI (Cloud AI), you will be a core technical contributor within WBD's AI Center of Excellence (CoE).

This role is designed for a hands-on senior architect who operates at the intersection of enterprise architecture, applied AI, Generative AI, agentic AI, and cloud platform engineering. You will translate complex business challenges into scalable AI solution architectures, production-ready platforms, and reusable technical patterns that drive measurable enterprise value.

• Define end-to-end solution architecture for enterprise AI, GenAI, and agentic AI applications aligned to business and technology strategy.
• Translate business workflows and operational challenges into scalable AI solution patterns, including autonomous and semi-autonomous agent use cases.
• Partner with product, engineering, data, and business teams to move AI and GenAI use cases from concept and pilot into production at enterprise scale.
• Create reusable architecture patterns, accelerators, reference implementations, and technical standards to speed AI adoption across teams.

• Design and govern the cloud architecture for AI/ML/GenAI platforms across AWS and Snowflake.
• Define scalable patterns for compute, storage, networking, identity, security, environment setup, and enterprise integration across development, test, and production environments.
• Build architecture patterns for MLOps and LLMOps covering deployment, experimentation, model lifecycle management, monitoring, retraining, and version control.
• Ensure platform reliability, resilience, observability, compliance, and cost optimization for AI solutions in production.
• Establish architecture standards and reference frameworks that accelerate AI solution delivery across business domains.

• Collaborate with Data Engineering, Platform Engineering, DevOps, and Security teams to productionize AI and GenAI solutions in scalable cloud environments.
• Design CI/CD and automation patterns for model deployment, prompt/version management, testing, release control, and operational support.
• Implement monitoring for model drift, prompt drift, performance degradation, usage patterns, system health, and data integrity.
• Support governance, risk management, and responsible AI initiatives by embedding security, privacy, compliance, and auditability into solution design.
• Innovation & Technical Leadership
• Stay current with advancements in foundation models, multimodal AI, agent frameworks, orchestration patterns, and cloud-native AI services.
• Provide architecture guidance, design reviews, and technical mentorship across cross-functional teams.
• Contribute to enterprise-wide AI best practices, reusable frameworks, and technical decision-making to elevate the maturity of the AI ecosystem.

• Bachelor's degree, Master's degree, or higher in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative discipline.
• 8+ years of relevant experience in solution architecture, data science, machine learning, or AI engineering, with at least 2+ years of experience in Generative AI / LLM-based solutions.
• Demonstrated track record of designing and delivering production-grade AI/ML/GenAI solutions with measurable business impact.
• Strong experience in architecting enterprise AI solutions across business workflows, data ecosystems, and cloud platforms.
• Hands-on expertise in building and scaling GenAI and LLM applications, including prompt engineering, RAG architectures, semantic search, embeddings, and evaluation frameworks.
• Experience designing or supporting agentic AI systems, including orchestration, tool usage, memory, guardrails, and human oversight patterns.
• Deep understanding of cloud-native AI/ML architecture principles, including deployment patterns, platform reliability, observability, security, and cost optimization.
• Experience establishing reusable architecture patterns, technical standards, and governance controls for AI systems in enterprise environments.
• Strong collaboration skills with Product, Engineering, Data, Security, and Business teams in global, cross-functional settings.

• Hands-on experience supporting Generative AI and ML workloads on enterprise cloud platforms, including AWS and Snowflake
• Strong working knowledge of LLM-based systems, including model hosting, inference optimization, and integration into cloud-native architectures.
• Experience enabling RAG pipelines and GenAI applications through secure data access patterns, vector search, and enterprise data integration.
• Familiarity with embedding models, semantic search, and vector storage technologies, including OpenSearch vectors and Snowflake Cortex Search.
• Understanding of LLM evaluation considerations within platform architecture, including performance monitoring, cost optimization, and quality metrics.
• Experience designing MLOps and LLMOps architectures for model deployment, monitoring, retraining, and lifecycle governance.
• Strong expertise in AWS cloud architecture, including VPC, IAM, S3, ECS/EKS, SageMaker, Bedrock, and observability tooling.
• Hands-on experience with infrastructure as code, CI/CD pipelines, and containerized AI workloads (Docker, Kubernetes).
• Working knowledge of Snowflake architecture, including Snowpark, Snowpipe, Streams & Tasks, and Cortex AI.
• Deep understanding of Responsible AI, data security, privacy controls, and governance requirements for enterprise AI platforms.