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NVIDIA

Designs the chips and software that power AI

Lead Principal Engineer - Enterprise Agentic AI Platform

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A Bachelor's degree in Computer Science or a related field, or equivalent experience.
  • 15 or more years of experience building and shipping large-scale distributed systems, including significant hands-on coding in Python, Go, or similar systems languages.
  • A proven ability to transition quickly from an idea to a functional prototype and then to a robust, scalable platform solution.
  • A proven track record constructing agentic AI systems, including retrieval-augmented generation pipelines, long-lasting memory models, multi-agent management such as LangChain or LangGraph, tool frameworks, and evaluation infrastructure.
  • Expert-level depth in Kubernetes, containerized workloads, networking, APIs, and secure enterprise integration patterns.
  • Experience crafting benchmarking, regression testing, telemetry, and observability systems that measure agent quality, latency, cost, reliability, and safety.
  • Comprehensive knowledge of performance tuning in hybrid environments, including GPU-based inference systems.
  • Excellent collaboration skills, the ability to influence cross-functional collaborators and build positive relationships, and the ability to communicate complex architectural concepts clearly to technical and business audiences.

Responsibilities

  • Develop and deliver production-quality agentic AI systems end to end using Python and/or Go, covering Kubernetes deployment, agent runtimes, memory systems, orchestration, tool integration, and evaluation pipelines.
  • Define and advance the Enterprise Agentic AI architecture through practical implementations, reference systems, and production deployments.
  • Build and implement multi-agent orchestration patterns, including planner, executor, reviewer, and tool agents, using frameworks such as LangChain, LangGraph, or similar systems, with regression coverage and observability.
  • Run proofs of concept on emerging agent architectures and harden successful patterns into reusable platform services, APIs, software development kits, and developer templates.
  • Architect and implement data flywheels that improve agent quality through telemetry, benchmarking, automated evaluation, and structured feedback loops.
  • Embed security, guardrails, sandbox isolation, auditability, and policy enforcement into agent runtimes in partnership with security and governance teams.
  • Evaluate, integrate, and extend open-source and third-party agent platforms, and make build-versus-use decisions based on performance, scalability, control, and long-term platform ownership.
  • Collaborate with engineering, infrastructure, product, and business collaborators to align architectural direction with enterprise priorities and accelerate adoption.

Desired Qualifications

  • Proven experience delivering reusable developer-acceleration components such as software development kits, APIs, templates, reference implementations, and continuous integration/continuous delivery automation.
  • Experience integrating enterprise vector databases and retrieval systems, and working with agentic search and orchestration platforms such as Glean, Microsoft Copilot Studio, Google Agentspace, or similar enterprise AI ecosystems.
  • Experience embedding fine-grained policy enforcement, access controls, sandbox isolation, and audit trails directly into AI runtimes.
  • Experience optimizing model inference, batching strategies, memory utilization, and efficiency on NVIDIA hardware using GPU acceleration.
  • Evidence of meaningful open-source contributions, including core commits, maintainership, widely adopted libraries, or public technical artifacts demonstrating system-level depth.
  • A Master's degree or PhD.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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