Principal Software Engineering Lead — Enterprise Data Platforms

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Principal Software Engineer to lead engineering efforts in transforming enterprise systems with AI-infused automated solutions. This role involves architecting and delivering enterprise-grade AI applications and workflow platforms, building resilient end-to-end systems, and developing data integration capabilities for AI applications. Key responsibilities include building agentic workflow automation, defining agent interactions, and supporting orchestration patterns using frameworks like LangChain and LangGraph, while also operationalizing NVIDIA AI technologies.

What you'd actually do

  1. 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.
  2. Build resilient end-to-end systems across frontend, backend APIs, distributed services, data pipelines, and orchestration layers for secure enterprise use.
  3. Develop enterprise data integration capabilities across Databricks, Snowflake, and similar EDW/lakehouse platforms to unify, govern, and operationalize data for AI applications.
  4. 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.
  5. 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.

Skills

Required

  • 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.

What the JD emphasized

  • 15+ years building and operating production software
  • Familiarity with multi-agent systems, skills, MCP integrations, agent-to-agent tooling, and orchestration frameworks such as LangChain, LangGraph, or similar.
  • Establish strong engineering practices across testing, CI/CD, observability, rollout safety, incident response, and operational excellence.

Other signals

  • building agentic workflow automation
  • defining how agents, skills, MCP-based integrations, APIs, and agent-to-agent tooling work together
  • support modern orchestration patterns using frameworks such as LangChain, LangGraph