Senior Architect, Agentic AI for Marketing

NVIDIA•Us, CA

13d

About The Position

NVIDIA is seeking a Senior Architect to lead the development of next-generation Agentic AI platforms for NVIDIA Marketing. This role involves technical leadership and hands-on engineering to translate marketing needs into AI agents and reusable platform capabilities. The position focuses on areas like personalization, customer journeys, content intelligence, recommendations, campaign operations, and field enablement. The team has expertise in recommendation systems, RAG, embedding retrieval, model evaluation, conversational AI, model adaptation, and production infrastructure. The architect will provide technical direction, business translation, and platform leadership to build a durable agentic AI ecosystem for Marketing, working at the intersection of applied AI, agentic systems, marketing technology, enterprise data, and production platforms. Responsibilities include leading initiatives from discovery through deployment, evaluation, and scaling.

Requirements

A BS, MS, or PhD in Computer Science, AI/ML, Electrical Engineering, Data Science, a related technical field, or equivalent experience.
12+ years of experience in software engineering, AI/ML engineering, solutions architecture, applied AI, data platforms, or large-scale production systems.
Experience building and deploying applications involving LLMs, generative AI, RAG, recommendation systems, conversational AI, or agentic AI.
Strong programming skills in Python, with experience in APIs, Linux environments, distributed systems, containers, cloud-native infrastructure, and production debugging.
Understanding of agentic AI system design, including tool use, orchestration, planning, memory, retrieval, evaluation, guardrails, human approval, and failure handling.
Experience designing integrations between AI agents and enterprise tools using approaches like MCP, function calling, API gateways, or related interoperability patterns.
Experience developing conversational AI experiences grounded in structured or semi-structured data (e.g., text-to-SQL, intent classification, multi-turn dialogue, retrieval, connections to live data sources).
Experience with production AI or software infrastructure (e.g., model serving, Kubernetes, Docker, CI/CD, observability, monitoring, health checks, performance testing, cost optimization).
Demonstrated ownership of technical solutions across architecture, development, deployment, integration, and ongoing operations.
Ability to navigate ambiguous business problems, translate them into technical approaches, and communicate effectively with both technical and non-technical audiences.

Nice To Haves

Production multi-agent systems, agent runtimes, agent frameworks, or tool-using agents.
Agent frameworks such as NVIDIA NeMo Agent Toolkit, LangGraph, LlamaIndex, LangChain, CrewAI, Semantic Kernel, OpenAI Agents SDK, Google ADK, or similar technologies.
NVIDIA AI software, including NIM, NeMo, NeMo Retriever, NeMo Guardrails, NeMo Agent Toolkit, Nemotron, Triton, NVIDIA AI Enterprise, or GPU-enabled Kubernetes environments.
MCP or agent-tool interoperability, including authenticated tool routing, server registries, enterprise tool catalogs, or policy-aware agent gateways.
Agent evaluation and observability, including traces, tool-call monitoring, offline and online evaluations, regression testing, quality dashboards, or business outcome measurement.

Responsibilities

Lead the architecture and delivery of Agentic AI solutions supporting NVIDIA Marketing, including personalization, content discovery, campaign intelligence, recommendations, internal copilots, workflow automation, and customer-facing AI experiences.
Translate business and marketing needs into practical agent architectures, defining goals, tools, retrieval, memory, planning, human-in-the-loop workflows, evaluation criteria, and production operating models.
Advance NVIDIA Marketing’s agentic AI platform by building capabilities such as agent-tool gateways, multi-agent orchestration, conversational data assistants, recommendation APIs, embedding pipelines, contextual retrieval, model adapters, catalog intelligence, and production AI infrastructure.
Shape the platform roadmap across capabilities like agent registries, tool catalogs, permission models, memory and state services, evaluation frameworks, observability, reusable agent patterns, and lifecycle management.
Partner with engineers to design reliable interfaces for agent invocation, tool execution, response formats, memory, state management, and integrations with marketing platforms, analytics systems, chat experiences, and content repositories.
Establish production practices for agentic systems covering evaluation, regression testing, observability, latency, cost, reliability, safety, access controls, auditability, fallback behavior, and incident response.
Guide technical tradeoffs across model quality, retrieval precision, inference cost, throughput, latency, personalization, data freshness, privacy, security, and business impact.
Build prototypes, reference architectures, technical blueprints, and reusable components to transition ideas into scalable production systems.