This role pays more than
90%
of similar roles. Most pay
$177,250–$246,150
— the shaded band above.
At the midpoint, this role pays about
$290k
versus about
$212k
for comparable roles.
Based on 239 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
Senior Architect, Agentic AI for Marketing will join the marketing technology team to lead the architecture and delivery of agentic AI solutions. This role involves translating marketing opportunities—such as personalization, content intelligence, and campaign operations—into reliable AI agents and reusable platform capabilities. The successful candidate will build out a durable ecosystem featuring multi-agent orchestration, retrieval-augmented generation (RAG), conversational data assistants, and production infrastructure. Key technical requirements include proficiency in Python, Linux environments, distributed systems, and containerized cloud-native infrastructure. The role focuses on solving complex problems at the intersection of applied AI, enterprise data, and marketing technology by developing tools for memory management, tool execution, and evaluation frameworks. You will navigate trade-offs between model quality, inference cost, and reliability while building prototypes and technical blueprints to move innovative ideas into scalable production systems.
Translate marketing requirements into technical architectures for personalized content, campaign operations, and customer journeys.
Design and deliver Agentic AI solutions including multi-agent orchestration, tool gateways, and conversational data assistants.
Develop reusable platform capabilities such as agent registries, memory services, and evaluation frameworks.
Partner with engineers to design reliable interfaces for agent invocation, state management, and marketing system integrations.
Establish production standards for agentic systems covering observability, latency, cost optimization, and safety guardrails.
Create technical blueprints, prototypes, and reference architectures to move AI concepts into scalable production environments.
Manage technical trade-offs between model quality, retrieval precision, inference costs, and data privacy.
What we're looking for
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, distributed systems, containers, and cloud-native infrastructure.
Understanding of agentic AI system design including tool use, orchestration, planning, memory, retrieval, evaluation, guardrails, and failure handling.
Experience designing integrations between AI agents and enterprise tools using MCP, function calling, API gateways, or related interoperability patterns.
Experience developing conversational AI experiences grounded in structured or semi-structured data.
Experience with production AI or software infrastructure including model serving, Kubernetes, Docker, CI/CD, observability, and cost optimization.
Experience with multi-agent systems, agent runtimes, or specific frameworks like LangGraph, LlamaIndex, or CrewAI (preferred).
Familiarity with NVIDIA AI software such as NIM, NeMo, Triton, or GPU-enabled Kubernetes environments (preferred).