Senior Software Engineer, Engineering & Operations

NBCUniversal Advertising Products & Solutions (AP&S) is responsible for the product development and project management of NBCUniversal’s full advertising technology suite. From sales support to campaign execution, delivery, and billing, our division services both internal and external customers in support of NBCUniversal’s $7B+ annual advertising business. Together, we’re building the platform that powers the future of advertising at NBCU. We are seeking a Senior Data Engineer, Engineering & Operations to lead the Engineering & Automation pillar. Reporting to the Sr. Director of Engineering & Operations, you will lead the engineering and operational excellence behind our data collaboration ecosystem. You will be instrumental in designing and implementing scalable data architectures and driving automation through AI agents and self-service tooling, translating prototypes into robust production solutions. We are seeking a Senior Software Engineer to join the Engineering & Automation pillar. Reporting to the Senior Director of Engineering & Operations, this role will build production-grade software, reusable Python libraries, and AI-enabled automation systems that support NBCUniversal’s data collaboration ecosystem. The ideal candidate is a strong Python engineer who can design reliable systems, productize repeatable workflows, and build AI agents that help automate complex engineering and operational tasks across clean rooms for audience activation, measurement, and reporting. Responsibilities AI Agent & Automation Engineering Design and build internal AI agents and automation workflows using technologies such as LangChain, LangGraph, Snowflake Cortex, LlamaIndex, or similar frameworks, to support planning, tool use, retrieval, validation, and human-in-the-loop execution where appropriate. Develop reusable tools, APIs, and components that engineers can compose into new agentic workflows. Implement retrieval-augmented generation workflows, context management strategies, and prompt patterns that improve accuracy, reliability, latency, and cost efficiency. Agent Evaluation & Reliability Build evaluation harnesses, regression tests, and monitoring patterns for AI-agent behavior. Define and track metrics such as task completion, groundedness, response accuracy, latency, cost, and failure rate. Design guardrails and validation patterns to reduce hallucinations, unsafe outputs, and unreliable automation behavior. Partner with engineering and operations teams to move AI workflows from prototype to production-ready systems. Software Engineering & Platform Development Design, build, and maintain production-grade Python applications, libraries, and services. Champion object-oriented design principles, including encapsulation, abstraction, inheritance/composition, reusable interfaces, and clean separation of concerns to improve maintainability and extensibility. Champion software engineering best practices including modular design, automated testing, CI/CD, code reviews, observability, and documentation. Create reusable engineering patterns that reduce bespoke development effort and improve consistency across partner engagements. Collaborate with product, engineering, operations, and data platform teams to translate repeatable business needs into scalable technical solutions. Audience & Measurement Productization Build reusable Python libraries that support clean room capabilities across first-party audience workflows and core measurement use cases, including audience onboarding, ingestion, indexing, activation, campaign and impression delivery analysis, reach and frequency, attribution, and incrementality. Abstract complex analytical and data collaboration workflows into repeatable, self-service components for internal teams and external partners. Enable configurable feature deployment so new audience and measurement capabilities can be delivered quickly and consistently across partners. Technical Mentorship Mentor engineers through code reviews, technical design discussions, and operational best practices. Help establish engineering standards for AI-assisted workflows, agentic system design, reusable libraries, and production automation. Promote a culture of reliability, maintainability, and continuous improvement.