Senior Staff Forward Deployed Engineer, Enterprise AI and Automation

NVIDIA•Us, CA

About The Position

NVIDIA's invention of the GPU fueled the AI revolution and transformed how the world builds intelligent systems. Today, NVIDIA is transforming how AI accelerates every aspect of our own business. Our Enterprise AI organization develops production-grade AI agents and intelligent applications that improve engineering productivity. We are seeking a Senior Staff Forward Deployed Engineer to drive Enterprise AI transformation across NVIDIA. In this highly technical, hands-on leadership role, you will partner directly with business organizations to identify high-impact opportunities, design and build production AI solutions, and drive enterprise adoption that delivers measurable business outcomes. Within NVIDIA Enterprise AI, forward deployment means embedding with internal teams to deeply understand their workflows, systems, and domain constraints, and engineering solutions with them on shared enterprise AI infrastructure. Forward Deployed Engineers work at the intersection of software engineering, AI, product development, and business transformation. They tackle complex operational challenges while converting successful deployments into reusable platform capabilities that accelerate AI innovation across NVIDIA. As a Senior Staff engineer, you will influence technical strategy across multiple enterprise domains, mentor engineers, and help establish Forward Deployed Engineering as a core engineering capability within NVIDIA's Enterprise AI organization.

Requirements

BS, MS, or equivalent experience in Computer Science, Software Engineering, or a related field, with 12+ years of experience building and operating complex, large-scale production software systems.
Proven track record to take products from ambiguous problem definition through architecture, prototyping, productionization, and long-term operation, with clear end-to-end ownership of technical and business outcomes.
Strong software engineering skills and the ability to contribute across the application stack while making sound architectural tradeoffs around scalability, reliability, performance, security, maintainability, and development velocity.
Hands-on experience building production AI applications using LLMs, agentic architectures, RAG, tool use, workflow orchestration, memory, evaluation systems, guardrails, or intelligent automation. This role emphasizes building complete AI-powered products and systems rather than model research or experimentation in isolation.
Strong product and user-experience judgment, including the ability to turn complex technologies and workflows into intuitive, high-quality products that users trust and adopt.
Demonstrated success working directly with business partners to understand complex requirements, translate ambiguous problems into scalable technical solutions, and deliver measurable business impact.
Deep technical leadership, architectural judgment, and communication skills, with a track record of influencing engineering strategy and collaborating effectively across engineering, product, and business teams.
Ability to quickly learn new business domains and become a trusted technical partner for enterprise organizations.

Nice To Haves

Experience delivering AI solutions for enterprise business domains such as Supply Chain and IT Operations.
Experience deploying multi-agent systems, enterprise AI assistants, workflow automation platforms, AI copilots, or autonomous business processes into production environments.
Hands-on experience with NVIDIA AI technologies including Nemotron, NIM, NeMo Agent Toolkit, AI Blueprints, or other NVIDIA AI software platforms.
Experience building reusable frameworks, platforms, or shared engineering capabilities that enable multiple products, business domains, or organizations.
Demonstrated fluency with AI-assisted development tools and coding agents, with a self-directed builder attitude and a track record of rapidly turning ideas into working prototypes while maintaining strong standards for architecture, testing, security, code review, and maintainability.

Responsibilities

Own technical delivery for enterprise domains such as Supply Chain, IT Operations, Site Reliability, Network Infrastructure, Finance, HR, and Security, leading solutions from business discovery and architecture through development, deployment, adoption, and continuous improvement.
Design, build, and operate full-stack AI systems using Python, TypeScript, PostgreSQL, vector databases such as Milvus, and Kubernetes.
Integrate commercial and open-weight language and multimodal models through standard and OpenAI-compatible APIs, selecting models based on capability, quality, latency, cost, security, and operational requirements.
Rapidly prototype new AI capabilities, validate them with users, and translate real-world feedback into intuitive, high-quality products while maintaining production engineering standards.
Own the production success of deployed solutions by measuring adoption, evaluation results, reliability, operational performance, and business impact while establishing strong practices for testing, observability, CI/CD, security, performance, and operational readiness.
Identify reusable patterns across business domains and work with platform engineering teams to turn successful deployments into shared Enterprise AI services and frameworks that inform broader strategy and architecture.
Use AI-assisted development tools extensively while remaining accountable for architecture, implementation quality, testing, code review, security, and maintainability.
Provide technical leadership through architectural guidance, mentoring, and scalable engineering practices.