AI Engineer at NBCUniversal3

The forward-deployed AI Engineer designs, builds, and ships AI-powered solutions, including agentic systems, that transform workflows and unlock new capabilities across NBCUniversal News Group. This role is deeply technical, highly collaborative, and execution-focused, with success measured by production adoption and measurable operational impact. This individual works end-to-end from discovery and prototyping through deployment and iteration, combining LLMs, retrieval, data integration, and orchestration frameworks to deliver reliable solutions in production. This is not a backend-only role; the engineer will build across services, integrations, and user-facing experiences as needed. Engineers will work closely with editorial, product, data, and technology teams to co-create solutions in a fast-moving, real-time business environment where speed and timely delivery are critical. Key Responsibilities: Design, Build, and Ship AI + Agentic Solutions • Architect and implement end-to-end AI applications from concept to production, including agentic workflows. • Build systems using LLMs and multimodal models, retrieval and vector databases, and orchestration frameworks (e.g., LangChain/LangGraph, Google ADK), integrated with internal tools and data. • Own the full lifecycle: discovery, prototyping, validation, deployment, monitoring, iteration, and support.Execute Fast in a High-Velocity Environment • Deliver solutions on accelerated timelines aligned to editorial and business urgency. • Prioritize evidence-driven iteration over perfection; make clear tradeoffs across scope, speed, and quality. • Adapt quickly to changing requirements and real-world feedback, keeping stakeholders aligned.Hands-On Technical Execution • Write production-quality code and integrate APIs across internal platforms and external providers (e.g., Google, OpenAI, Databricks, AWS, Microsoft). • Make pragmatic architectural decisions balancing speed, scale, security, reliability, and cost. • Build reusable components, templates, and playbooks that accelerate future development and reduce friction for downstream teams.Apply the Right AI to the Right Problems • Evaluate model capabilities and select appropriate AI tools based on workflow needs, risk profile, and operational constraints. • Combine services across selected providers into cohesive, reliable systems with strong observability and clear failure modes. • Implement evaluation frameworks to measure quality, reliability, and impact, and to guide iteration.Responsible AI, Safety, and Governance • Design guardrails for agentic systems, including tool safety, permissioning, data handling, and human-in-the-loop review where needed. • Partner with Standards, Legal, Security, and GRC to ensure compliant data use, responsible deployment, and audit-ready practices. • Establish monitoring and incident response patterns to detect issues and maintain trust and reliability at scale.Collaborate Deeply Across Teams • Work directly with editorial, product, and business partners to shape solutions from requirements through rollout. • Collaborate with our Data team on data quality, retrieval strategies, evaluation design, and measurement. • Partner with the CTO and platform engineering organizations to harden solutions for production environments. • Communicate clearly with technical and non-technical audiences, translating complexity into decisions and action.Stay Ahead of the Market • Track new model releases, vendor updates, and emerging patterns in agentic AI. • Run targeted experiments with new capabilities and share timely recommendations that influence roadmaps and standards.Leverage AI in the Role • Use AI coding agents and copilots to accelerate development, debugging, refactoring, test generation, and documentation. • Apply AI to speed analysis, coordination, and knowledge capture while maintaining security, quality, and compliance.