Senior Software Engineer, Platform Engineering

NVIDIA•Santa Clara, CA

3d•$168,000 - $322,000

About The Position

NVIDIA is hiring a hands-on Senior Full-Stack Software Engineer to build next-generation AI platforms and products that improve business efficiency and productivity. The role involves developing AI products using retrieval-augmented generation (RAG), agentic AI paradigms, third-party platforms, and open-source repositories. As a technical leader, the engineer will help shape the architecture, development, and scaling of software systems and collaborate with Cloud, AI/ML, and Generative AI teams in an agile environment to meet current and evolving business needs.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
At least 8 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.
Familiarity with Linux, embedded systems, firmware, and hardware/software integration.
Deep knowledge of IT Service Management (ITSM) and ServiceNow.
Hands-on experience with test automation, production testing, and automation frameworks.

Nice To Haves

Experience with ServiceNow HR, Security, or Finance modules.
Experience improving enterprise efficiency and employee experience through Generative AI solutions.
Experience with cloud platforms, Kubernetes, and Docker.
Fascination with unique and difficult problems, resilience, and persistence in pursuing solutions.
Self-motivation and a drive to bring work to completion.

Responsibilities

Apply test automation, production testing, and automation frameworks.
Collaborate with cross-functional teams to deliver end-to-end intelligent experiences across applications.
Integrate agents with enterprise data sources, APIs, and internal microservices to enable real-world actions.
Architect and build agentic AI systems that use LLMs for reasoning, planning, and tool orchestration across enterprise workflows.
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.
Lead complex technical initiatives and mentor high-performing teams.