Principal Software Engineer

NVIDIA•Santa Clara, CA

5d•$248,000 - $391,000

About The Position

NVIDIA is hiring a deeply technical, creative, hands-on full-stack developer to build AI platforms and products that improve business efficiency and productivity. The role involves applying retrieval-augmented generation (RAG), agentic AI paradigms, third-party platforms, and open-source repositories to deliver AI products. As a technical leader, the engineer will shape the architecture, development, and scaling of software systems, collaborating with Cloud, AI/ML, and Generative AI teams in an agile environment to meet current and evolving business needs.

Requirements

BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent experience.
15+ years of experience building large-scale distributed systems and cloud-native applications; Python is preferred.
Strong experience designing and deploying LLM-powered systems, including RAG, tool use, and agent-based architectures.
Deep understanding of agentic AI paradigms, including planning, memory, tool invocation, and multi-step reasoning.
Experience with agent frameworks and orchestration tools such as LangChain or similar.
Proven ability to design systems with high reliability, scalability, and performance.
Hands-on experience deploying applications in Kubernetes environments.
Experience building systems in cloud and hybrid environments.
Strong programming skills across multiple languages and modern software stacks.
Proven track record leading complex technical initiatives and mentoring high-performing teams.
Excellent problem-solving, communication, and system design skills.

Nice To Haves

Experience enhancing enterprise efficiency and employee experience through Generative AI solutions.
Fascination with unique and difficult problems, with resilience and persistence in pursuing solutions.
Experience with cloud platforms, Kubernetes, and Docker.
Self-motivation and a drive to get work to completion.
Excellent programming, debugging, performance analysis, and test design skills using Python.

Responsibilities

Apply test automation, production testing, and automation frameworks.
Integrate agents with enterprise data sources, APIs, and internal microservices to enable real-world actions.
Collaborate with cross-functional teams to deliver end-to-end intelligent experiences across applications.
Ensure reliability, safety, and performance of autonomous systems operating at scale.
Architect and build agentic AI systems that use LLMs for reasoning, planning, and tool orchestration across enterprise workflows.
Design and implement multi-agent frameworks that support coordination, delegation, and dynamic task execution.
Develop scalable platforms for retrieval-augmented generation (RAG), long-term memory, and contextual reasoning.
Build reusable infrastructure for agent orchestration, tool integration, evaluation, and observability.
Establish best practices for agent evaluation, guardrails, and human-in-the-loop workflows.
Mentor engineers and provide technical leadership in designing complex distributed AI systems.
Stay current with advancements in LLMs, agent frameworks, tool use, reasoning architectures, and open-source ecosystems.