Staff DevOps Engineer

As the Staff DevOps Engineer, you will be responsible for leading our DevOps and platform engineering initiatives, fostering a culture of automation, continuous integration, observability, reliability, and delivery. Your efforts will support consumer data-driven advertising and marketing products, standardized consumer identity solutions, machine learning platforms, and emerging AI-powered applications across NBCUniversal and its brands. You will partner with engineering, data, security, and AI teams to design and operate scalable cloud platforms that enable application development, analytics, machine learning, and AI workloads. Your expertise in AWS services, Infrastructure as Code, container orchestration, observability, platform automation, and cloud architecture will be critical in building secure, resilient, and cost-effective environments that accelerate innovation while maintaining enterprise governance and operational excellence. You will operate in an individual contributor capacity, but as a Staff Engineer, you will lead and mentor more junior DevOps Engineers, fostering a culture of innovation, continuous improvement, operational excellence, and platform-first thinking. Responsibilities: Develop and lead the implementation of DevOps, Platform Engineering, and Site Reliability Engineering (SRE) practices to improve the efficiency, scalability, reliability, and security of cloud-based applications and services. Design, build, and maintain robust CI/CD pipelines that automate software delivery, infrastructure deployment, platform provisioning, and operational workflows. Architect, deploy, and manage scalable AWS cloud infrastructure supporting applications, data, machine learning, and AI workloads. Implement and manage containerization and orchestration platforms (Docker, Kubernetes, EKS) to ensure consistent, secure, and resilient environments across development, testing, and production. Establish and maintain Infrastructure as Code (Terraform, CloudFormation) and Git Ops practices to improve platform consistency, governance, and operational efficiency. Build and support ML Ops and AI platform capabilities, including model deployment automation, inference platform operations, AI workflow orchestration, secure API integration, and lifecycle management for AI-enabled services. Enable secure integration of AI platforms, LLM-based applications, vector databases, agent frameworks, and machine learning services while ensuring compliance with enterprise security and governance standards. Develop observability solutions using logging, monitoring, tracing, and operational analytics platforms to proactively detect performance issues, infrastructure anomalies, security risks, and AI workload health concerns. Design scalable cloud architectures that support high availability, disaster recovery, cost optimization, and AI workload scalability. Partner with Security, Data, and Architecture teams to implement cloud security controls, identity and access management, secrets management, and compliance requirements across traditional and AI-enabled platforms. Drive automation of infrastructure operations, environment provisioning, platform governance, and operational processes to reduce manual effort and improve developer productivity. Maintain comprehensive documentation, operational runbooks, architecture standards, and platform governance processes.