We're looking for a Senior Architect to help shape the next generation of Agentic AI platforms for NVIDIA Marketing. In this role, you'll combine technical leadership with hands-on engineering. You'll translate marketing opportunities-including personalization, customer journeys, content intelligence, recommendations, campaign operations, and field enablement-into reliable AI agents and reusable platform capabilities. You'll join a team with strong foundations in recommendation systems, retrieval-augmented generation (RAG), embedding-based retrieval, model evaluation, conversational AI, model adaptation, and production infrastructure. Your role will be to provide senior technical architecture, business translation, and platform leadership, helping the team evolve from high-value AI applications into a durable agentic AI ecosystem for Marketing. Our team works at the intersection of applied AI, agentic systems, marketing technology, enterprise data, and production platforms. You'll have the opportunity to lead complex initiatives from early discovery through architecture, prototyping, evaluation, deployment, observability, governance, and scale.
What You'll Be Doing:- Lead the architecture and delivery of Agentic AI solutions that support 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, including 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 on 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 such as 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 across 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 that help move promising ideas into scalable production systems.
What We Need to See:- 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 one or more areas such as 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, along with experience working with 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 such as MCP, function calling, API gateways, or related interoperability patterns.
- Experience developing conversational AI experiences grounded in structured or semi-structured data. This may include text-to-SQL, intent classification, multi-turn dialogue, retrieval, or connections to live data sources.
- Experience with production AI or software infrastructure, such as model serving, Kubernetes, Docker, CI/CD, observability, monitoring, health checks, performance testing, or 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.
Ways to Stand Out from the crowd::- Production multi-agent systems, agent runtimes, b 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.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 28, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
