Principal Software Engineering Lead — Enterprise Data Platforms

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 NVIDIA.

Key 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.

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 Have:

• 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.