Lead Principal Engineer, Enterprise Agentic AI Platform
$272k - $431k • Remote • US, CA, Santa Clara
Posted 1d ago
About the job
NVIDIA IT's Enterprise AI & Automation team is seeking a Principal or Distinguished Engineer to architect and build enterprise-grade agentic AI systems. This role involves hands-on coding in Python and/or Go to develop production AI agents that securely integrate with enterprise systems, enhancing employee efficiency and business outcomes across various departments. The ideal candidate will be a technical leader who can translate new agent development patterns into scalable platform capabilities, define NVIDIA's enterprise agent architecture through practical implementations, and drive the full agent development lifecycle from creation to continuous enhancement.
This position is for a deeply involved technical leader who writes code daily and thrives in ambiguous environments, rapidly moving from concepts to operational systems. You will invent systems incorporating persistent memory, controlled runtimes, strict assessment, and GPU-powered performance to ensure agents are intelligent, trackable, protected, and production-ready.
Responsibilities
- Develop and deliver production-quality agentic AI systems from start to finish using Python and/or Go, covering Kubernetes deployment, agent runtimes, memory systems, orchestration, tool integration, and evaluation pipelines.
- Define and advance NVIDIA’s Enterprise Agentic AI architecture through practical implementations, reference systems, and production deployments.
- Build and implement multi-agent orchestration patterns using frameworks such as LangChain, LangGraph, or similar systems, with strong regression coverage and observability.
- Run fast, high-quality POCs on emerging agent architectures and harden successful patterns into reusable platform services, APIs, SDKs, and developer templates.
- Architect and implement data flywheels that continuously improve agent quality through telemetry, benchmarking, automated evaluation, and structured feedback loops.
- Embed security, guardrails, sandbox isolation, auditability, and policy enforcement directly into agent runtimes.
- Evaluate, integrate, and extend open-source and third-party agent platforms, making disciplined build-vs-use decisions.
- Collaborate closely with engineering, infrastructure, product, and business collaborators to align architectural direction with enterprise priorities and accelerate adoption.
Requirements
- Bachelor’s degree in Computer Science or related field or equivalent experience; Master’s or PhD preferred.
- 15+ years of experience building and shipping large-scale distributed systems with significant hands-on coding in Python, Go, or similar systems languages.
- Proven skill in quickly transitioning from an idea to a functional prototype and then to a robust, scalable platform solution.
- Proven track record in constructing agentic AI systems, including RAG pipelines, long-lasting memory models, multi-agent management, tool frameworks, and evaluation infrastructure.
- Expert-level depth in Kubernetes, containerized workloads, networking, APIs, and secure enterprise integration patterns.
- Experience crafting benchmarking, regression testing, telemetry, and observability systems that measure agent quality, latency, cost, reliability, and safety.
- Comprehensive knowledge of performance tuning in hybrid environments, including GPU-based inference systems.
- Excellent collaboration skills with the ability to influence cross-functional collaborators, build positive relationships, and clearly communicate complex architectural concepts to both technical and business audiences.
Benefits
- Base salary range: 272,000 USD – 431,250 USD