Principal Software Engineer — Agentic AI Applications and Foundations
AI summary of the role
NVIDIA's Enterprise AI team seeks a principal-level, hands-on engineering leader to harden production AI agents and architect next-generation agent infrastructure.
What you’ll do
- Improve reliability, performance, observability, release confidence, and end-user experience across desktop, web, and service-based AI products.
- Design and build resilient frontends, backend APIs, distributed services, data flows, and deployment systems that scale to enterprise use.
- Establish strong patterns for testing, debugging, CI/CD, safe rollout, auto-update mechanisms, monitoring, incident response, and operational excellence.
- Build reusable capabilities for multiple agent domains including orchestration services, memory and context services, evaluation frameworks, telemetry, and policy-aware tool integration.
What you’ll bring
- 15+ years building and operating production software systems with significant architecture and full-stack delivery experience.
- Solid experience building modern applications across frontend, backend, and platform layers (TypeScript/JavaScript, React, Electron, Python, Go, Java, APIs, distributed infrastructure).
- Proven track record taking complex products from prototype to reliable, secure, well-operated production systems with deep expertise in testing, release engineering, observability, and incident response.
- Experience building shared services, internal platforms, SDKs, or core infrastructure used by multiple teams or products.
Technologies
TypeScript · JavaScript · React · Electron · Python · Go · Java · Nemotron · NVIDIA AI Blueprints · NeMo · NIM · TensorRT-LLM
About NVIDIA
Designs and manufactures GPUs and system-on-chips powering data centers, AI workloads, gaming, autonomous vehicles, and HPC. The foundational hardware for modern deep learning.
Public
Source and classification
Internal deployment & tooling · Evidence for this classification:
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC-gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU-accelerated deep learning ignited modern AI—the next era of computing—with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” Our Enterprise AI team builds intelligent AI agents that transform how NVIDIA operates — from smart personal assistants and engineering-productivity tools to data-driven analytics and supply-chain optimization. These agents are live, in production, and used across the company. Now we need a principal-level, hands-on engineering leader to make them bulletproof and to architect the next generation of agent infrastructure. This is not a research role. This is a role
More from the job description
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC-gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU-accelerated deep learning ignited modern AI—the next era of computing—with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” Our Enterprise AI team builds intelligent AI agents that transform how NVIDIA operates — from smart personal assistants and engineering-productivity tools to data-driven analytics and supply-chain optimization. These agents are live, in production, and used across the company. Now we need a principal-level, hands-on engineering leader to make them bulletproof and to architect the next generation of agent infrastructure. This is not a research role. This is a role for someone who obsesses over reliability, polish, and user trust — and who has the full-stack depth to harden production systems and the architectural vision to ensure they scale. What you'll be doing: Improve reliability, performance, observability, release confidence, and end-user experience across desktop, web, and service-based AI products. Design and build resilient frontends, backend APIs, distributed services, data flows, and deployment systems that scale to enterprise use. Establish [... source excerpt omitted ...] elemetry, and policy-aware tool integration. Help validate and operationalize technologies such as Nemotron, NVIDIA AI Blueprints, and related platform capabilities in enterprise production settings. Codify architecture, shared components, documentation, and operational playbooks; mentor engineers; and create foundations that are durable, reusable, and broadly owned. Define the core architecture for how AI agents discover one another, collaborate securely, build trust, and operate under enterprise governance. Partner closely with domain AI engineers, product managers, designers, infrastructure teams, IT, and research to deliver measurable outcomes across employee productivity, [... source excerpt omitted ...] ing efficiency, AIOps, and enterprise operations. What we need to see: BS, MS, or equivalent experience in Computer Science or a related field. 15+ years building and operating production software systems, including significant experience leading architecture and delivery across the full stack. Solid experience building modern applications across frontend, backend, and platform layers. This may include technologies such as TypeScript/JavaScript, React, Electron or similar desktop frameworks, Python, Go, Java, APIs, data systems, and distributed infrastructure. Proven track record taking complex products from prototype to reliable, secure, well-operated production systems. Deep
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