Principal Software Engineer — Agentic AI Applications And Foundations

at Nvidia

USD 272,000-431,200 per year

SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 4 API @ 6 Agentic AI @ 7 CI/CD @ 7 Communication @ 7 Debugging @ 7 Design Patterns GPU @ 4 Go @ 6 Java @ 6 JavaScript @ 6 LLM @ 4 Leadership @ 7 Observability @ 4 Python @ 6 RAG @ 4 React @ 6 Security @ 3 TensorRT @ 4 TypeScript @ 6

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 so our Agentic AI applications behave like mature software, not prototypes.
Build reusable capabilities that support multiple agent domains, including orchestration services, deep-agent workflows, memory and context services, evaluation frameworks, telemetry, 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, engineering efficiency, AIOps, and enterprise operations.

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.

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.