Principal Software Engineering Lead — Enterprise Data Platforms
at Nvidia
USD 248,000-391,000 per year
Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Security @ 4
Go @ 4
TypeScript @ 4
Python @ 4
Java @ 4
CI/CD @ 7
Leadership @ 4
Communication @ 7
Data Engineering @ 4
JavaScript @ 4
React @ 4
ServiceNow @ 6
API @ 4
Databricks @ 4
LLM @ 4
Snowflake @ 4
Salesforce @ 6
Codex @ 4
Claude Code @ 4
Observability @ 7
AI @ 4
Robotics @ 4
Agentic AI @ 4
Data Pipelines @ 4
TensorRT @ 4
LangChain @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
For over 25 years, NVIDIA has been at the forefront of transforming computer graphics, PC gaming, and accelerated computing. The company is leveraging AI to power new classes of computing across robotics, autonomous systems, and more. This role will own engineering efforts across NVIDIA enterprise systems, partnering with IT leadership to transform reactive support into strategic, AI-infused automated systems that balance speed, security, and user experience.
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, 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 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; 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 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 and LangGraph.
- Strong judgment, communication, and cross-functional leadership skills, with the ability to remain highly hands-on.
- Experience leveraging AI coding tools (e.g., Cursor, Claude Code, Codex) to improve engineering productivity and development workflows.
- Establish strong engineering practices across testing, CI/CD, observability, rollout safety, incident response, and operational excellence.
Ways to stand out
- 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.
Benefits
- Eligibility for equity and company benefits (see NVIDIA benefits page for details).
Compensation
- Base salary range: 248,000 USD - 391,000 USD (determined based on location, experience, and pay of employees in similar positions).
Additional information
- Applications accepted at least until April 26, 2026.
- This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer committed to diversity and inclusion.