• Automate cloud infrastructure provisioning using Terraform and Ansible, writing IaC that supports Simple, Secure, and Scalable deployments • With an emphasis on networking, directly support platform engineering partners and application development teams in troubleshooting, provisioning and maintaining various enterprise infrastructure • Architect, optimize, and maintain secure network infrastructure in Azure, AWS, & GCP • Configure and manage virtual networks and subnets, including IP addressing, routing, firewall rules, and load balancing • Collaborate with security teams to enforce Zero Trust networking, IAM, and data encryption standards, as well as general security framework adherence • Through IPAM, maintain a network topology that provides for integration testing among development teams while remaining flexible enough to unify additional environments as merger and acquisition requires • Solution and implement Direct Connect, Express Route, and VPN technologies to establish secure, reliable and resilient connections • Work closely with Security and Compliance to ensure security framework adherence (SOC2, Hi-Trust, FEDRAMP etc.) • Provide self-service infrastructure governance and automation for the development teams, enabling them to focus on delivering business value as efficiently as possible

• Design and manage GCP project structure, networking, and core infrastructure • Own Kubernetes cluster infrastructure (provisioning, upgrades, scaling, reliability) • Define and maintain infrastructure-as-code (Terraform) standards and patterns • Improve system-level architecture for scalability, resilience, and performance • Build reusable infrastructure patterns that product teams can safely adopt • Reduce friction for developers while maintaining strong guardrails • Partner with engineering teams to improve how services are deployed and operated • Manage and evolve base observability systems (metrics, logging, tracing foundations) • Build and maintain infrastructure for secrets management, IAM, and security primitives • Ensure infrastructure is secure by default, not by exception • Improve infrastructure reliability and reduce systemic risk • Lead and contribute to incident response when infrastructure is involved • Identify and eliminate infrastructure-level failure modes and bottlenecks • Drive cloud cost visibility and optimization • Build systems and practices that balance performance, reliability, and cost • Partner with leadership to align infrastructure investment with business needs • Shape infrastructure strategy across teams and services • Drive adoption of standards and best practices across engineering • Identify systemic issues and lead cross-team improvements.

• Contribute to a platform that automates GPU asset provisioning, configuration, and lifecycle management across cloud providers • Build end-to-end automation of datacenter operations, break/fix, and lifecycle management for large-scale Machine Learning systems • Implement monitoring and health management capabilities that enable reliability, availability, and scalability of GPU assets • Manage NVLINK topography across GPU clusters • Build automated test infrastructure to qualify distributed systems for operation • Collaborate with engineering teams to ensure software integration from hardware to AI training applications

• Provision and configure large GPU clusters and compute resources for LLM training, finetuning, and inference workloads. • Develop and optimize LLM model-serving infrastructure, including deployment and optimization of various inference frameworks. • Lead model lifecycle management including versioning, checkpointing and reproducibility across training and inference deployments. • Design and champion robust evaluation frameworks to assess model performance, accuracy, and reliability, ensuring AI systems are consistently at production-ready standards. • Identify and address GPU utilization and GPU memory efficiency bottlenecks and apply techniques like quantization, batching, and caching. • Architect and maintain data platforms and pipelines specifically designed to support LLMs, Retrieval-Augmented Generation (RAG), and AI Agentic Systems at scale. • Deliver production-ready code with a focus on performance, maintainability, and testing rigor, ensuring the ability to ship fast without compromising quality. • Apply expertise in data modeling, normalization, and semantic cataloging for AI/ML workloads. • Define and enforce best practices for MLOps/DataOps surrounding LLMs, including monitoring, observability, and zero-touch recovery mechanisms for AI services. • Document architectural designs thoroughly and communicate technical decisions clearly to stakeholders. • Collaborate across the organization with Data Scientists, Product Managers, and other engineering teams to transform research prototypes into robust, production-grade services.