Lead Architect: Agentic AI for Marketing Platforms

NVIDIA; United States; On-site; USD 224,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA seeks a Senior Architect to shape the next generation of Agentic AI platforms for NVIDIA Marketing. You’ll translate marketing opportunities—including personalization, customer journeys, content intelligence, and recommendations—into reliable AI agents and reusable platform capabilities, leading from discovery through deployment.

You’ll join a team with strong foundations in recommendation systems, RAG, embedding-based retrieval, and production infrastructure, delivering scalable,

Qualifications

• BS/ MS/ PhD in CS/AI or related field, or equivalent exp.
• 12+ years of software/AI/solutions architecture or related.
• Experience with LLMs, RAG, conversational AI, or agentic AI.
• Strong Python and production-grade tooling skills.

Responsibilities

• Lead architecture and delivery of Agentic AI solutions for NVIDIA Marketing.
• Translate marketing needs into practical agent architectures.
• Advance platform through tool gateways, multi-agent orchestration, and APIs.
• Shape roadmap for registries, memory, evaluation, and observability.
• Collaborate on interfaces for agent invocation, memory, and integrations.
• Establish production practices for evaluation, latency, and security.
• Guide tradeoffs across model quality, cost, and performance.
• Build prototypes, blueprints, and reusable components.

Skills

Python

Distributed systems

AI/ML architecture

Leadership

Education

BS/MS/PhD in CS/AI or related

Tools

Kubernetes

Docker

CI/CD

APIs

Job description

NVIDIA seeks a Senior Architect to shape the next generation of Agentic AI platforms for NVIDIA Marketing. You’ll translate marketing opportunities—including personalization, customer journeys, content intelligence, and recommendations—into reliable AI agents and reusable platform capabilities, leading from discovery through deployment.

You’ll join a team with strong foundations in recommendation systems, RAG, embedding-based retrieval, and production infrastructure, delivering scalable,