Staff Software Engineer (GenAI Innovation/ Forward deployed)

Responsibilities

• Rapidly research and test new AI technologies, translating raw capability into measurable business ROI.
• Evaluate proposed AI solutions critically, and be willing to say "no" when the underlying problem isn't actually an AI problem.
• Build out backends and data layers by creating new business logic that leverages API standards, NBCU gateways, and established best practices and patterns.
• Deploy and maintain databases and storage for our applications across both SQL and NoSQL environments.
• Design features that span from the application UI down to the underlying APIs.
• Uphold high standards of code quality through rigorous testing and adherence to code standards — including code that's agent-written.
• Develop automated tests for your code, ensuring every function, service, and object works reliably with your team's systems and the broader NBCUniversal portfolio, with attention to cross-device and cross-browser compatibility.
• Collaborate effectively within a multidisciplinary, matrixed organization, using strong interpersonal skills to navigate system complexity and deploy solutions efficiently.
• Deploy to cloud-based platforms and troubleshoot application, cloud, and configuration issues as needed.
• Use code- and test-generation tools to significantly accelerate the delivery of the features and components you build.
• 6+ years of experience in backend or full-stack development, demonstrated through a comprehensive portfolio and related experience.
• Demonstrated systems-thinking capabilities, with the demonstrated ability to design for systemic problems (rather than symptoms).
• Proven ability to independently bring a basic application from idea to delivery, utilizing cross-functional collaboration as needed.
• Experience with Python for application development, AWS, database systems, and SQL.
• Experience with the Python ecosystem, including tools for build, test and deployment.
• Deep understanding of ML practices as they relate to foundation model integration (e.g., tooling and instrumentation, feature/model selection, RAG patterns) and some fine-tuning, deep learning, and training/algorithm insight.