Principal Software Engineering Lead — Enterprise Data Platforms

NVIDIA•Us, CA

About The Position

For over 25 years, NVIDIA has been at the forefront of transforming computer graphics, PC gaming, and accelerated computing, driven by a legacy of continuous innovation and exceptional talent. We are now leveraging the immense potential of AI to usher in the next era of computing, where our GPUs power the "brains" of computers, robots, and autonomous vehicles that can comprehend the world. This pioneering work demands vision, innovation, and the world's best talent. Join our diverse and supportive environment, where NVIDIANs are inspired to excel and make a profound global impact. We're hiring a Principal Software Engineer to own the engineering efforts across NVIDIA enterprise systems. You'll partner with IT leadership to transform reactive support into strategic, AI infused automated systems balancing speed, security, and an exceptional user experience for NVIDIAs.

Requirements

BS, MS, or equivalent experience in Computer Science, Software Engineering, Data Engineering, or a related field.
15+ years building and operating production software, with significant experience leading architecture across application, platform, and data systems.
Strong background in enterprise data architecture, including integration, pipelines, governance, lineage, observability, and modern EDW/lakehouse platforms such as Databricks, Snowflake, or similar systems.
Hands-on experience building modern full-stack and platform systems using technologies such as TypeScript/JavaScript, React, Python, Go, Java, APIs, and distributed infrastructure.
Familiarity with multi-agent systems, skills, MCP integrations, agent-to-agent tooling, and orchestration frameworks such as LangChain, LangGraph, or similar.
Strong judgment, communication, and cross-functional leadership skills, with the ability to stay highly hands-on.
Leverage AI coding tools such as Cursor, Claude Code, Codex, and similar systems to improve engineering productivity and development workflows.
Establish strong engineering practices across testing, CI/CD, observability, rollout safety, incident response, and operational excellence.

Nice To Haves

Experience creating reusable connectors, canonical models, or integration layers across systems of record.
Experience delivering workflow automation that improves employee productivity, enterprise operations, IT workflows, or service operations.
Experience building reusable infrastructure for agentic AI applications, including orchestration, memory/context, evaluation, and policy controls.
Familiarity with identity, governance, trust, and permission models for enterprise agent collaboration.
Experience with NVIDIA AI technologies such as NeMo, NIM, Nemotron, TensorRT-LLM, or AI Blueprints.

Responsibilities

Lead the architecture and delivery of enterprise-grade AI applications and workflow platforms that connect to core systems of record such as SAP, Salesforce, ServiceNow, Confluence, Asana, and others.
Build resilient end-to-end systems across frontend, backend APIs, distributed services, data pipelines, and orchestration layers for secure enterprise use.
Develop enterprise data integration capabilities across Databricks, Snowflake, and similar EDW/lakehouse platforms to unify, govern, and operationalize data for AI applications.
Build agentic workflow automation for high-value enterprise jobs to be done across business systems, including retrieval, approvals, case management, triage, task coordination, and operational workflows.
Define how agents, skills, MCP-based integrations, APIs, and agent-to-agent tooling work together securely across enterprise platforms, with strong governance, permissions, and auditability.
Support modern orchestration patterns using frameworks such as LangChain, LangGraph, and similar tooling for multi-step and multi-agent workflows.
Help operationalize NVIDIA AI technologies such as Nemotron and AI Blueprints in enterprise production environments.
Mentor and lead engineers, codify shared architecture and reusable components, and partner across product, infrastructure, IT, enterprise data, and research teams to deliver measurable outcomes.