Senior Software Engineer— Agentic AI Workflow Applications

NVIDIA•Santa Clara, 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 Senior Software Engineer (AI) to build the AI and agentic 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.
8 plus years of experience building and operating production software systems, with strong hands-on experience across backend, platform, and data-intensive applications.
Strong background in enterprise data architecture and engineering, including integration, pipelines, governance, lineage, observability, and modern EDW/lakehouse platforms such as Databricks, Snowflake, or similar systems.
Hands-on experience building modern software using technologies such as TypeScript/JavaScript, React, Python, Go, Java, APIs, and distributed systems.
Experience designing or building AI powered workflow automation and agentic systems that operate across enterprise tools with appropriate controls and human oversight where needed.
Familiarity with multi-agent systems, skills, MCP integrations, agent-to-agent tooling, and orchestration frameworks such as LangChain, LangGraph, or similar.
Experience using AI-assisted development tools such as Cursor, Claude Code, Codex, or similar coding agents.
Strong technical judgment, problem-solving, communication, and collaboration skills.
Contribute to strong engineering practices across testing, CI/CD, observability, rollout safety, incident response, and operational excellence.
Help operationalize NVIDIA AI technologies such as Nemotron and AI Blueprints in enterprise production environments.

Nice To Haves

Background with building agentic AI and enterprise data products or platforms on Databricks, Snowflake, or similar EDW/lakehouse stacks.
Experience creating reusable connectors, canonical models, or integration layers across systems of record.
Experience delivering AI powered workflow automation that improves employee productivity, enterprise operations, IT workflows, or service operations.
Background with NVIDIA AI technologies such as NeMo, NIM, Nemotron, TensorRT-LLM, or AI Blueprints.

Responsibilities

Design and build enterprise-grade AI applications and workflow platforms that connect to core systems of record such as SAP, Salesforce, ServiceNow, Confluence, and Asana.
Develop resilient systems across frontend, backend APIs, distributed services, data pipelines, and orchestration layers for secure enterprise use.
Build enterprise data integration capabilities across Databricks, Snowflake, and similar EDW/lakehouse platforms to unify 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.
Implement integrations across agents, skills, MCP-based integrations, APIs, and agent-to-agent tooling with strong attention to governance, permissions, and auditability.
Apply orchestration patterns using frameworks suchs as LangChain, LangGraph, and similar tooling for multi-step and multi-agent workflows.
Partner with product, infrastructure, IT, enterprise data, and research teams to deliver measurable business outcomes, while contributing reusable components and engineering best practices.