• Design and develop serverless and containerized services using Go and Python • Build and operate microservices using cloud-native compute platforms across AWS, Azure, and GCP (e.g., Lambda, Azure Functions, ECS/Fargate, managed container services) • Implement Infrastructure as Code using Terraform to manage multi-account and multi-project cloud environments • Develop and maintain CI/CD pipelines using Jenkins and CloudOS • Build event-driven workflows using cloud-native messaging and orchestration services • Implement scalable data persistence using managed databases and object storage services • Contribute to security and compliance controls • Implement monitoring, logging, and tracing using cloud-native observability tools and custom metrics • Support AI-assisted observability initiatives, including intelligent alerting, anomaly detection, and signal correlation using telemetry data • Optimize services for reliability, performance, and cost across cloud providers • Participate in on-call rotations and production incident response • Build and enhance automated cloud account and project provisioning workflows • Develop self-service APIs for infrastructure requests and governance • Implement cross-account and cross-project identity and access controls • Create reusable Terraform modules and service templates • Build and maintain Docker images and manage deployments via container registries

• Drive the technical vision and architecture for Aerial Capture, driving clean code standards, setting technical direction, and making key architectural decisions. • Serve as a primary code contributor, lead projects end-to-end, and proactively coordinate work to ensure predictable delivery, technical quality, and the quick unblocking of teammates. • Raise the technical bar across the team by providing hands-on mentorship, pairing, and timely, direct technical feedback to foster engineer growth. • Take ownership of system health, including reliability and maintainability. Champion the reduction of technical debt and measure outcomes to drive value throughput. • Act as the technical voice for the team in cross-functional forums, communicating decisions, trade-offs, and project status clearly to Engineering Managers and stakeholders.

• Enhance NVIDIA's future GPU Networking offerings for accelerating AI workloads. • Lead vision, architecture and design of such technologies. • Lead proof-of-concept development to evaluate and drive such technologies. • Identify and evaluate new technologies, innovations and partner relationships for alignment with our technology roadmap and business value. • Work with the community and maintainers to drive strategic technologies.

• Primarily responsible for analyzing data integrity challenges including investigating, correcting, and monitoring data to help identify and address key data issues. • Facilitate effective communication with client project stakeholders regarding project status and recommendations. • Craft client code that is not just efficient, but also performant, testable, scalable, secure, and of the highest quality. • Actively participate in accurate planning and estimation efforts, utilizing project methods and tools. • Proficiently gather requirements and organize/present developed features for clients. • Execute complex activities within the current methodology and quality standards, showcasing success across diverse engagements. • Promote client success across the team by collaborating with engineers, designers, and managers to understand user pain points, anticipate potential problems, and iterate on solutions that drive client success. • Engage in agile software development, including daily stand-ups, sprint planning, team retrospectives, and other governance activities. • Actively participate in the Engineering Practice community, mentoring others through Communities of Practice (CoPs) or on project teams, and supporting the growth of technical capabilities. • Independently drive project delivery within defined architecture, demonstrating autonomy and accountability in all stages from conceptualization to deployment.