NVIDIA

• Build and deploy scalable ML/AI and optimization models to enhance demand forecasting, optimize capacity allocation, and develop user-specific feature engineering for real-time cloud gaming services. • Develop reusable framework deployments for data ingestion, processing, and analysis to support dynamic user interventions for targeted business outcomes. • Acquire and apply domain knowledge of the product and software stack to identify and drive the resolution of data inconsistencies and improve model performance, especially in the context of optimization outcomes. • Identify, analyze, and interpret trends or patterns in complex data sets using supervised and unsupervised learning techniques, informing prescriptive solutions. • Design and implement improvements to real-time prescriptive scheduling pipelines, using techniques like linear programming and constraint optimization, to enhance capacity utilization and user retention. • Improve productivity of the organization by mining petabytes of data for actionable insights for business and engineering, often through prescriptive recommendations. • Collaborate with a variety of partners to understand requirements, design robust solutions, and guide the team to deliver impactful results. • Leverage agentic AI to deliver best-in-class automation and programming solutions for complex analytical problems.

• Be a technical specialist on GPU and networking products, partnering with account, program, architecture, and engineering teams to support design-in, integration readiness, issue resolution, and successful ramp execution. • Actively establish and nurture technical relationships with partner engineering teams, technical leaders, internal stakeholders, and cross-functional launch teams. • Identify customer architectures and key product requirements in the CSP/ODM AI market to successfully implement NVIDIA's solutions. • Provide primarily onsite technical support, with remote and travel-based support as business needs require, to debug hardware, firmware, software, networking, and system integration issues. • Lead the product across design-in, NPI, rack integration, validation, production ramp, sustaining support, and lifecycle transitions. • Develop technical solutions including hardware & software demos and example system builds. • Offer technical enablement to internal technical teams, account teams, program teams, and partner-facing stakeholders. • Establish strong communication channels and collaborative relationships with internal teams to ensure a positive customer experience.

• Be a technical specialist on GPU and networking products, partnering with account, program, architecture, and engineering teams to support design-in, integration readiness, issue resolution, and successful ramp execution • Actively establish and nurture technical relationships with partner engineering teams, technical leaders, internal stakeholders, and cross-functional launch teams • Identify customer architectures and key product requirements in the CSP/ODM AI market to successfully implement NVIDIA's solutions • Provide primarily onsite technical support, with remote and travel-based support as business needs require, to debug hardware, firmware, software, networking, and system integration issues • Lead the product across design-in, NPI, rack integration, validation, production ramp, sustaining support, and lifecycle transitions • Develop technical solutions including hardware & software demos and example system builds • Offer technical enablement to internal technical teams, account teams, program teams, and partner-facing stakeholders • Establish strong communication channels and collaborative relationships with internal teams to ensure a positive customer experience.

• Provide onsite support for large datacenter deployments of NVIDIA AI solutions. • Solving complex hardware and deployment issues. • Be a technical specialist on GPU and networking products directly supporting sales account managers, working closely with the team to deliver outstanding AI datacenter solutions. • Actively establish the technical relationship with our customer’s engineers, management, and architects at focus accounts. • Identify customer architectures and key product requirements in the enterprise AI market. • Provide technical and sales training to the direct sales team and channel partners. • Establish positive relationships and communication channels with internal teams. • Up to 30% travel may be required.

• Analyze and demonstrate HW fault metric compliance • Propose new methods and refine existing practices • Carry out analysis using classical approaches such as fault trees alongside model-based techniques • Develop automation to support the analysis of complex multi-layered systems • Analyze and interpret internal and external safety documentation for improvements • Ensure integration of the System, Software and Hardware safety analysis methodologies

• Refine developer, user, and agent journeys: Understand how developers, enterprise platform teams, partners, and customers, and their respective agents, consume NVIDIA AI software, then craft clear technical journeys supported by documentation, code examples, demos, and deployment guidance. • Showcase enterprise AI software workflows: Build demos, reference examples, notebooks, and sample applications that show how NVIDIA AI software components work together across model development, inference, RAG, agentic AI, evaluation, deployment, and operations. • Build compelling technical assets: Accelerate adoption by creating public-facing content such as product documentation, deployment guides, reference architectures, tutorials, blog posts, whitepapers, technical presentations, webinars, demo videos, and code examples. • Develop automation and docs-as-code workflows: Create repeatable examples and publishing workflows using Git-based documentation, CI/CD, scripts, templates, and AI-assisted docs or skills where appropriate. • Enable the field and partner ecosystem: Support solution architects, sales teams, cloud partners, ISVs, and ecosystem teams with technical assets that help them explain, deploy, and integrate NVIDIA enterprise AI software. • Collaborate across the stack: Work closely with Technical Marketing Engineering, Product Management, Engineering, Developer Relations, Field, and Marketing teams to turn product capabilities into practical adoption paths. • Capture feedback and improve the product experience: Use customer, partner, developer, and field feedback to identify gaps in usability, examples, documentation, deployment patterns, and product workflows. • Engage the developer and open source community: Advocate for NVIDIA AI software in developer, cloud-native, and open source ecosystems, encouraging adoption through clear examples and practical technical storytelling.

• 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

• Build and maintain infrastructure across bare-metal, virtualized, and containerized environments using advanced technologies and high-end hardware. • Drive continuous improvements across CI/CD pipelines and automation workflows. • Provide infrastructure and automation support for development and verification teams, enabling efficient development and testing processes. • Support development and integration activities for next-generation infrastructure and software platforms. • Improve reliability, scalability, and operational efficiency across Linux- and Windows-based environments.

• Build storage technologies, client libraries, and filesystem frameworks that help AI workloads access data across object stores, file systems, and hybrid cloud infrastructure. • Develop high-performance storage paths for training and inference workflows, including data loading, checkpointing, caching, POSIX-style access, and object-store integration. • Build observability systems that diagnose storage bottlenecks, attribute GPU idle time to I/O behavior, and expose actionable telemetry through production monitoring stacks. • Improve performance, scalability, and reliability of storage systems serving massive datasets, deep directory trees, and high-concurrency AI workloads. • Work closely with internal AI teams, platform teams, SRE, and operations to validate storage behavior against real workloads and production environments. • Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, performance, and verification.

• Recruit and Mentor architects personally and professionally, aligning with Industry and Company goals • Work across teams to drive Financial Services Industry strategy • Industry leadership and vision integrating NVIDIA full-stack to support financial services use cases and sub-verticals • Provided technical leadership on all NVIDIA products pertinent to the industry, directly supporting Industry Business Development and Sales to achieve design wins and execute industry strategy