Senior Architect, AI Solutions Engineering

NVIDIA•Us, CA

23h

About The Position

NVIDIA is seeking an AI Solutions Architect to join its Infrastructure Planning and Process Team! This role will focus on the extensive scale-up of key AI solutions for NVIDIA's internal cloud infrastructure. IPP (Infrastructure, Planning and Process) is a global organization within NVIDIA, working closely with various teams such as Graphics Processors, Mobile Processors, Deep Learning, Artificial Intelligence, and Driverless Cars to meet their infrastructure needs. The cloud services support nearly half a million automated jobs daily on five thousand servers, enhancing the productivity of thousands of NVIDIA software developers worldwide. The cloud hosts a diverse mix of machines and devices with various operating systems (Windows/Linux/Android) and hardware platforms, including NVIDIA GPUs and Tegra processors. As an AI Solutions Architect, you will manage the tools NVIDIAns use to deliver solutions quickly, and identify any gaps in these tools. You will also understand overall movement of data in the entire platform, identifying bottlenecks, defining solutions, developing key pieces, writing APIs, and owning deployment. You will collaborate with internal and external development teams to discover opportunities and solve complex problems. Your role will also involve guiding engineers in solving complex problems, developing acceptance tests, and reviewing their work and test results. Exceptional technical leadership, communication, organizational, and analytical skills are required, along with a passion for solving large and complex problems, e.g. Peta Bytes of fast storage, Million cores, 100,000 builds and 100,000 tests. What you’ll be doing: Serve as an Architect developing internal AI systems used by thousands of NVIDIANs globally. Identify gaps and issues and resolve ones are better suited for AI solutions versus conventional approaches. Further divide the AI category into 'buy versus build' options by researching available tools in the market. Align with teams across Nvidia to establish overall AI system goals and break them down into specific objectives for each sub-system. Drive, motivate, convince, and mentor sub-system leads to achieve improvements with agility and speed. Identify performance bottlenecks and optimize the speed and cost efficiency of AI development and testing systems. Drive the planning of software/hardware capacity, covering both internal and public cloud, addressing the balance between time and utilization. Introduce technologies enabling massively parallel systems to improve turnaround time by an order of magnitude. Collaborate with AI product vendors to gain deep insights of the AI industry, and share them with leaders and developers internally.

Requirements

BS EE/CS or equivalent experience with 10+ years of systems software development with at least 1 year of experience in developing/exploring AI.
Development with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Fine-Tuning LLMs, AI Agentic workflows, LangChain, LangGraphs, and Cascading models.
Experience in deploying in hybrid, multi-cloud architecture and edge computing.
Extensive experience architecting and shipping large-scale distributed software systems.
Ability to identify gaps and bottlenecks, and develop solutions to optimize performance.
Strong programming and software development skills in JAVA, Python, Shell-script along with good understanding of distributed systems and REST APIs.
Experience in working with SQL/NoSQL database systems such as MySQL, Cassandra, MongoDB or Elasticsearch.
Excellent knowledge and working experience with Docker containers and Virtual Machines.
Good background of Cloud technologies like: OpenStack, Docker, Kubernetes, Chef/Puppet, Hadoop/Ceph/SwiftStack, LXC, Git, Perforce, JFrog, Kafka.
Ability to work across organizational boundaries optimally to improve alignment and productivity between teams in a multi-national, multi-time-zone corporate environment.

Nice To Haves

MS or PhD in EE/CS
Depth in AI, Machine Learning and Deep Learning algorithms and techniques.
Strong collaborative and interpersonal skills, with a consistent record of guiding and influencing others in dynamic environments.
Experience developing large-scale software systems using service-oriented architecture under real-time performance requirements.
Background in designing high-performance, scalable software systems with a strong focus on hardware cost optimization.

Responsibilities

Serve as an Architect developing internal AI systems used by thousands of NVIDIANs globally.
Identify gaps and issues and resolve ones are better suited for AI solutions versus conventional approaches.
Further divide the AI category into 'buy versus build' options by researching available tools in the market.
Align with teams across Nvidia to establish overall AI system goals and break them down into specific objectives for each sub-system.
Drive, motivate, convince, and mentor sub-system leads to achieve improvements with agility and speed.
Identify performance bottlenecks and optimize the speed and cost efficiency of AI development and testing systems.
Drive the planning of software/hardware capacity, covering both internal and public cloud, addressing the balance between time and utilization.
Introduce technologies enabling massively parallel systems to improve turnaround time by an order of magnitude.
Collaborate with AI product vendors to gain deep insights of the AI industry, and share them with leaders and developers internally.