Senior AI Engineer, Agents and Developer Workflows: NVIDIA

NVIDIA is hiring senior software engineers for its Infrastructure, Planning and Process Team (IPP) to accelerate AI adoption across various engineering workflows within the company. The IPP group works with various teams such as Graphics Processors, Mobile Processors, Deep Learning, and Artificial Intelligence to cater to their infrastructure and software development needs. As a senior engineer, you will create and establish tools and software solutions that leverage Large Language Models and agentic AI to automate software engineering workflows and enhance engineer productivity.

About the Role:

What You’ll Be Doing:

Develop and implement solutions throughout software development lifecycles to improve developer efficiency and accelerate feedback loops.

Design, develop, and deploy AI agents to automate software development workflows and processes.

Continuously measure and report on the impact of AI interventions on metrics such as cycle time and mean time to recovery (MTTR).

Build and deploy predictive models to identify high-risk commits and forecast potential build failures.

Research emerging AI technologies and engineering best practices to continuously evolve the development ecosystem.

What We Need To See:

BE (MS preferred) or equivalent experience in EE/CS with 10+ years of work experience.

Deep practical knowledge of Large Language Models (LLMs), Machine Learning (ML), and Agent development.

Strong background in implementing AI solutions to solve real-world software engineering problems.

Hands-on experience with Python/Java/Go.

Experience with SQL/NoSQL database systems (e.g., MySQL, MongoDB, Elasticsearch).

Full-stack, end-to-end development expertise (React/Angular, Python/Go/Java).

Experience with CI/CD tools such as Jenkins, Gitlab CI, Packer, or Terraform.

Good understanding of distributed systems and microservice architecture.

Ways To Stand Out From The Crowd:

Proven expertise in applied AI, particularly using Retrieval-Augmented Generation (RAG) and fine-tuning LLMs on enterprise data.

Experience delivering large-scale, service-oriented software projects under real-time constraints.