• Investigate small to large scale security and engineering issues to determine impact and risks to platform data • Conduct response and recovery efforts across complex systems and large data sets • Examine systems and applications to understand current security posture • Advocate for best-security practices to engineering organization • Identify, design, and partner with teams to implement security and data improvement projects

• Own and deliver medium-to-large platform projects from implementation through production • Build and maintain internal platform services and tooling that improve developer experience and delivery velocity • Partner with application teams to unblock delivery and align platform capabilities with developer needs • Drive cloud cost efficiency and optimization across our cloud and infrastructure • Improve operational excellence through observability, automation, and incident response • Help evolve CI/CD pipelines and GitOps-based workflows • Mentor junior engineers through code reviews and knowledge sharing • Participate in on-call rotations and incident response

• Architect, implement, and manage cloud-native infrastructure (Kubernetes, Terraform, Terragrunt, Docker, Azure) to support scalable and reliable platforms. • Champion SRE principles: drive service reliability, availability, and performance through automation, monitoring, and incident management. • Lead platform automation, CI/CD, and infrastructure-as-code initiatives to improve deployment velocity and system consistency. • Own observability, monitoring, and alerting using Datadog and related tools, ensuring actionable insights and rapid incident response. • Collaborate with software engineers to ensure robust integration between application and platform layers, and to improve developer experience (DevX). • Provide expert support for critical incidents and production issues, demonstrating high ownership and urgency (including out-of-hours support when required). • Mentor and guide engineers in platform, SRE, and DevOps best practices, fostering a culture of reliability and continuous improvement. • Identify and address gaps in tooling, automation, and platform reliability to proactively improve engineering outcomes.

• Architect and manage Kubernetes clusters tailored to AI/ML workloads. • Implement Run:ai and operators for GPU resource orchestration and workload scheduling. • Develop and maintain Python-based automation scripts and ML pipelines; automate infrastructure provisioning with Terraform and configuration management with Ansible. • Create and manage Jupyter Notebooks for experimentation and collaboration. • Integrate and optimize NVIDIA Enterprise Suite components (CUDA, NeMo Framework, Triton, TensorRT, GPU drivers) for accelerated computing. • Establish and maintain MLOps best practices for model lifecycle management, CI/CD, and monitoring (e.g., MLflow, Kubeflow). • Work closely with data scientists and platform engineers to ensure efficient resource utilization and scalability across environments.