> Markdown version of [/jobs/ext/3697796-principal-software-engineer-agentic-ai-applications-and-foundations](https://www.wearedevelopers.com/jobs/ext/3697796-principal-software-engineer-agentic-ai-applications-and-foundations). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Principal Software Engineer - Agentic AI Applications and Foundations

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Salary:** $272,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Application Lifecycle Management, User Authentication, Data Systems, Software Design Patterns, Programming Tools, Distributed Systems, Python (Programming Language), Performance Tuning, Systems Integration, TypeScript, Enterprise Software Applications, ReactJS, Retrieval-Augmented Generation, Large Language Models, Electron (Programming Language), Backend, Agentic-AI, Information Technology, Front End Software Development, TensorRT, Nim (Programming Language), AIOps, Human in the Loop, Golang
- **Published:** October 12, 2026
- **Apply:** https://www.juju.com/job/1_ScrapingHub33470-JR2015726

## About the Role

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. + Familiarity with enterprise application deployment, security, authentication, device management, and application lifecycle management. + 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 expertise in testing strategy, release engineering, observability, performance tuning, and incident response. + Experience building shared services, internal platforms, SDKs, or core infrastructure used by multiple teams or products. + Working knowledge of modern AI application patterns such as LLM-powered applications, RAG, tool use, CLI-based workflows, reusable skills, MCP-based integrations, evaluation loops, memory systems, and agentic workflows.
You do not need to be a research scientist, but you should know how to build reliable, production-grade systems around AI. + Strong judgment, communication, and cross-functional leadership skills, with the ability to influence across teams while remaining highly hands-on.
Ways to stand out from the crowd: + Experience hardening desktop or client applications at scale, including installers, auto-update systems, crash recovery, and enterprise distribution. + A track record of improving engineering velocity and consistency through common frameworks, platform services, design patterns, and developer tooling. + Experience building reusable infrastructure for AI products, such as orchestration layers, memory/context services, evaluation platforms, human-in-the-loop workflows, or policy and safety controls. + Familiarity with identity, discovery, trust, reputation, or graph-based systems relevant to large-scale agent collaboration. + Experience with GPU-accelerated systems or NVIDIA AI technologies such as NeMo, NIM, Nemotron, TensorRT-LLM, or AI Blueprints.

## Description

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, engineering efficiency, AIOps, and enterprise operations.