• 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.

• Design, develop and evolve solution architectures in cloud environments (AWS and Azure); • Work hands-on in the implementation and maintenance of solutions across multiple projects; • Model and document technical architectures, ensuring standardization and governance; • Support development and infrastructure teams in building scalable and secure solutions; • Provide technical leadership for architecture initiatives and support technical decision-making; • Develop and support DevOps strategies, CI/CD processes and infrastructure automation; • Build and support integrations between systems and APIs; • Work with Linux and Windows environments in corporate contexts; • Support architecture definitions oriented to microservices and application modularization; • Participate in agile team ceremonies and routines, contributing technical guidance; • Collaborate with multidisciplinary teams to understand technical and business requirements; • Propose continuous improvements to processes, tools and architectures; • Ensure best practices for security, availability, performance and observability of solutions; • Share technical knowledge and support the development of team members; • Assist in defining architectural standards and best practices for development and infrastructure.