Sr Data Governance Engineer (AI)

The Data and Analytics Team (D&A) at NBCUniversal is looking for a passionate Senior AI Data Governance Engineer who’s eager to participate in NBCU’s D&A journey to AI. In this role, you’ll design and implement governance guardrails for AI models and agents, ensuring secure, compliant, and scalable deployments. You’ll work at the intersection of cloud engineering, ML/AI Ops, and data governance, partnering with technologists across NBCU’s diverse portfolio - from streaming and broadcast to film studios and theme parks.. The D&A team focuses on future-proof data and analytics strategies, supporting NBCU’s vast portfolio - from broadcast, cable, news, streaming services, and sports networks to film studios, theme parks, and digital properties. We pride ourselves on delivering data that shapes strategic business decisions around content. Join us! Responsibilities Implement AI governance policies in collaboration with NBCU Legal, Privacy, and Cyber teams. Build monitoring and reporting frameworks for AI models and tools, emphasizing cost, tagging, and AI FinOps principles like usage tracking and optimization. Develop and manage ML/AI Ops pipelines including CI/CD for models using GitHub Actions or Jenkins, ensuring automation in model deployment and version control. Design and implement AI infrastructure as code through Terraform or AWS CloudFormation, promoting consistent and auditable environment management. Support multi-cloud AI workloads across AWS (SageMaker, IAM), GCP (Vertex AI), Azure (ML + Purview), Snowflake (Cortex), and Databricks (Genie/Agentbricks). Build best practices on AI model integration with Semantic Layer, including semantic models, YAML-based configurations, and API/SQL integrations. Build AI consumption and usage metrics dashboards with guardrails using tools like Llamaguard, LangGraph, and LangChain. Enable RBAC on AI models across tools/LLMs for Data & Analytics teams, enforcing IAM policies for secure access. Facilitate AI model cataloguing, lineage, and governance of Semantic Layer for Gen AI models. Facilitate build of training/synthetic data for AI model testing. Implement technical guardrails such as fairness constraints, bias detection, and transparency measures in AI tools/models. Conduct risk assessments and mitigation through regular audits of fairness, bias detection, and transparency. Coordinate new Data & Analytics POCs on AI features and promote AI governance at design. Build an AI Literacy framework (Tokens, Embeddings, Prompts, RAG, Grounding, Hallucination, Vectorization, Bias, etc.). Facilitate decision matrix on AI model implementation per use case. Engage in architecture design sessions, code reviews, and change management for AI model deployment.