Sr. Staff, Data Science & Applied AI

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

Who We Are…

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 (Gen 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.

You will serve as a senior individual contributor, partnering closely with Product, Engineering, Data, Security, and Business stakeholders to design, govern, and scale modern AI solutions across the organization. The role combines solution architecture leadership with deep expertise in GenAI application design, agentic systems, and AI cloud architecture on AWS and Snowflake.

Enterprise AI Solution Architecture

• 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.

Generative AI & LLM Applications

• Architect scalable GenAI applications for use cases such as enterprise copilots, semantic search, summarization, metadata enrichment, content intelligence, and document processing.
• Define reference architectures for Retrieval-Augmented Generation (RAG), prompt orchestration, model selection, context management, and response quality optimization.
• Develop and standardize evaluation frameworks to assess hallucination risk, answer quality, latency, cost efficiency, safety, and business relevance.
• Implement responsible AI controls, safety mechanisms, and governance practices to ensure compliant and reliable deployment of GenAI systems.
• Evaluate and recommend foundation models, embeddings, prompt strategies, and adaptation approaches based on business fit, performance, and cost.

Productionization, Engineering & Governance

• 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.

Qualifications & Experiences:

• 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 Gen…