Senior Software Engineer, Agentic AI and Observability

NVIDIA•Santa Clara, CA

23h•$168,000 - $270,250

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.