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Senior AI Workflow Engineer
| Company | NVIDIA |
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| Location | Santa Clara, CA, USA |
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| Salary | $184000 – $356500 |
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| Type | Full-Time |
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| Degrees | Bachelor’s, Master’s |
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| Experience Level | Expert or higher |
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Requirements
- BE (MS preferred) or equivalent experience in EE/CS with 10+ years of work experience.
- Well versed with Large Language Model (LLM), Machine Learning (ML), Agentic AI techniques.
- Hands-on experience in using large language models (LLMs) and implementing AI for software engineering workflows.
- Hands-on experience on Python/Java/Go with extensive python scripting experience.
- Experience in working with SQL/NoSQL database systems such as MySQL, MongoDB or Elasticsearch.
- Experience in Full stack development. Proficient in front-end (e.g., React, Angular, Vue.js, HTML, CSS, JavaScript), back-end (e.g., Node.js, Python/Django/Flask, Ruby on Rails, Java/Spring, .NET) development, database management (SQL/NoSQL), and deployment/hosting (e.g., AWS, Azure, GCP).
- Experience with tools for CI/CD setup such as Jenkins, Gitlab CI, Packer, Terraform, Artifactory, Ansible, Chef or similar tools.
- Good understanding of distributed systems, understanding of microservice architecture and REST APIs.
Responsibilities
- Design and implement AI-driven optimizations within software development workflows to enhance developer productivity, accelerate feedback loops, and improve release reliability.
- Experience designing, developing, and deploying AI agents to automate and software development workflows and processes.
- Continuously measure and report on the impact of AI interventions, demonstrating improvements in key metrics like cycle time, change failure rate, and mean time to recovery (MTTR).
- Create and deploy predictive models to identify high-risk commits, forecast potential build failures, and flag changes that have a high probability of failures.
- Conduct research on emerging technologies to recommend best practices and improvements.
Preferred Qualifications
- Proactively track AI tool and technology trends, build insights, and collaborate with development teams early to evangelize AI driven workflows NVIDIA adoption.
- Expertise in leveraging large language models (LLMs) and Agentic AI to automate complex workflows, with knowledge of retrieval-augmented generation (RAG) and fine-tuning LLMs on enterprise data.
- Prior development of a large software project using service oriented architecture operating with real time constraints.
