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 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.
Generative AI & Large Language Model (LLM) Expertise
- Hands-on experience designing and deploying Generative AI solutions using large language models (e.g., GPT-class models, open-source foundation models, or enterprise LLM platforms).
- Strong proficiency in prompt engineering, structured output design, few-shot learning strategies, and systematic prompt optimization.
- Experience building Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and enterprise data sources.
- Familiarity with embedding models, semantic search, and vector stores (e.g., Pinecone, Weaviate, OpenSearch, FAISS, or equivalent).
- Experience fine-tuning or adapting foundation models using parameter-efficient approaches (e.g., LoRA, adapters, instruction tuning).
- Understanding of LLM evaluation methodologies, including hallucination detection, bias assessment, response quality scoring, and cost-performance trade-offs.
- Exposure to multimodal AI (text, image, audio, video) and agent-based workflows is a plus.
- Experience working with enterprise AI platforms (e.g., AWS Bedrock, Azure OpenAI, Databricks Model Serving, Snowflake Cortex, or equivalent).
- Understanding of Responsible AI principles, data privacy considerations, and model governance requirements in regulated environments.
How We Get Things Done…
This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.
Championing Inclusion at WBD
Warner Bros. Discovery embraces the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, regardless of sex, gender identity, ethnicity, age, sexual orientation, religion or belief, marital status, pregnancy, parenthood, disability or any other category protected by law.
If you’re a qualified candidate with a disability and you require adjustments or accommodations during the job application and/or recruitment process, please visit our accessibility page for instructions to submit your request.