Nvidia Senior AI Infrastructure Engineer Tooling - DGX Cloud 

United States, Texas 

792297764

You will contribute to building robust, scalable systems that support a wide range of applied AI initiatives. These may include integrating LLMs via APIs, supporting internal ML models, working with AI agent frameworks, building data pipelines, and developing tools to improve AI reliability, observability, and deployment workflows. This is not a research or model training role. It is a practical engineering position focused on enabling and scaling real-world AI applications through solid infrastructure and backend development.

What you’ll be doing:

• Build AI-powered tools that enhance operational excellence by leveraging diverse operational data, supporting incident, change, and problem management workflows

• Collaborate closely with Incident Commanders, incident response, and SRE teams to integrate AI-driven automation and analytics into operational workflows

• Design, develop, and maintain backend systems and infrastructure using Go and Python to support internal AI tools and intelligent agents

• Build and maintain data pipelines using PySpark and related tools to support AI and analytics workflows

• Operate and scale infrastructure using Kubernetes, managing containerized AI services and automating pipeline deployments

• Work with vector databases to enable semantic search and retrieval-augmented generation use cases

• Integrate large language models, agent systems, and classical ML models into internal services and automation workflows

• Improve observability, deployment automation, and system reliability for AI-driven services

What we need to see:

• 8+ years of software engineering experience, with deep expertise in backend systems and infrastructure.

• Bachelor's degree or equivalent experience.

• Strong proficiency in Python and Go, with a track record of delivering reliable, scalable software solutions

• Experience designing scalable, maintainable backend systems and writing clear design documentation

• Deep experience with Kubernetes and cloud-native infrastructure

• Experience working with building, deploying, and maintaining ML models in production systems

• Familiarity with AI agent frameworks or orchestration tools

• Solid understanding of system observability, monitoring, and performance optimization

• Strong collaboration and communication skills to work across remote teams

Ways to stand out from the crowd:

• Proven experience delivering AI-driven features end-to-end, from infrastructure design to production deployment

• Experience with Temporal, Argo, or other workflow engines for multi-step or async job orchestration. Familiarity with vector databases and their applications in AI and LLM-powered systems

• Contributions to open-source projects related to AI infrastructure, backend development, or developer tooling