Ready to develop the future of AI at NVIDIA? Join our BizApps SRE team to advance the Agentic AI Factory model, a critical initiative aimed at fast development, deployment, and operation of AI-powered applications across the enterprise. As a Software Engineer, you will be positioned at the intersection of agentic AI and Business applications. You will guarantee that our AI agents and automation workflows perform reliably, efficiently, and at scale. This opportunity lets you craft tooling and instrumentation that enable BizApps teams to release AI-powered products faster.
What you'll be doing:
Build and implement observability solutions for agentic AI applications in production, including metrics, tracing, logging, and alerting.
Develop and sustain deployment pipelines and reliability tools for the Agentic AI Factory model.
Partner with AI application teams to define SLOs/SLIs and ensure production readiness for new agent deployments.
Instrument LLM-based workflows for performance, cost, and quality monitoring, covering multi-step reasoning chains, token usage, and tool orchestration.
Drive incident response, root cause analysis, and reliability improvements for BizApps AI services.
Identify and close observability gaps outstanding to agentic AI systems, such as hallucination detection and orchestration failure tracing.
Work jointly with platform, data, and application engineering groups to integrate reliability throughout the development lifecycle.
What we need to see:
BS or MS in Computer Science, Software Engineering, or a related field (or equivalent experience).
8+ years of experience and strong software engineering skills in Python and experience with modern CI/CD practices.
Hands-on experience with container orchestration (Kubernetes, Docker) and cloud infrastructure.
Experience with observability and monitoring platforms such as Datadog, OpenTelemetry, Grafana, or Prometheus.
SRE / DevOps approach — taking ownership of production systems, automating toil, and building for reliability.
Excellent problem-solving skills and a proven track record of debugging complex distributed systems.
Ways to stand out from the crowd:
Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel) and agentic AI build patterns.
Experience operating ML/AI systems in production environments.
Familiarity with AI-specific observability challenges — latency profiling for inference, token economics, timely response quality monitoring.
Contributions to open-source observability or AI tooling projects.
Experience with infrastructure-as-code (Terraform, Pulumi) and GitOps or equivalent experience workflows.
Be part of a team that’s crafting the future of AI at NVIDIA!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 270,250 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor’s or master’s degree in Computer Science, Software Engineering, or a related field, or equivalent experience
- 8+ years of experience
- Strong software engineering skills in Python
- Experience with modern CI/CD practices
- Hands-on experience with Kubernetes and Docker
- Experience with cloud infrastructure
- Experience with observability and monitoring platforms such as Datadog, OpenTelemetry, Grafana, or Prometheus
- SRE or DevOps experience owning production systems, automating toil, and building for reliability
- Excellent problem-solving skills and experience debugging complex distributed systems
- Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel
- Experience operating ML or AI systems in production
- Familiarity with AI-specific observability challenges, including inference latency, token economics, and response quality monitoring
- Contributions to open-source observability or AI tooling projects
- Experience with infrastructure as code using Terraform, Pulumi, or equivalent
- Experience with GitOps or equivalent workflows
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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