Role at a glance
- Job function
-
Software Engineering & IT Cloud & Infrastructure Engineering
- Salary
- Not yet verified Check the description for pay details; an amount or pay period has not been verified.
- Location
- Santa Clara, California, United States
- Employment
- Full-time
- Experience
- Senior Level Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience), with 12+ years in software engineering — ideally in platform engineering, infrastructure, or developer tools
- Education
- Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience)
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Role Summary
Designs and builds infrastructure for NVIDIA’s AI agent ecosystem, providing services for teams to develop, deploy, orchestrate, and operate agents at production scale. The work spans agent lifecycle services, Kubernetes execution environments, deployment pipelines, messaging, data and context systems, and security controls.
What You'll Do
- Build platform services for the agent lifecycle, from registration through deployment, execution, and teardown.
- Architect multi-tenant Kubernetes execution environments with isolation, storage, policy enforcement, and identity propagation.
- Develop CI/CD pipelines and reusable deployment blueprints, and support multiple agent SDKs and harnesses.
- Build Kafka-based message pipelines and real-time event streaming, as well as data ingestion, access, and storage systems for agent...
- Implement session management, authentication, and secrets lifecycle capabilities, and partner with security teams on compliance and...
Generated from the employer's posting. Verify important details before applying.
View full postingQualifications
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent experience; 12+ years in software engineering. Required experience includes production AI agents; Kubernetes; Python and production APIs; distributed systems using Kafka, Redis, and MongoDB or PostgreSQL; GitLab CI and ArgoCD; AI data platform components and developer-facing APIs; OAuth 2.0, JWT, token exchange, and Vault. The role also requires a track record leading complex technical projects and strong interpersonal and design-documentation skills.
Required
- 12+ years in software engineering; platform engineering, infrastructure, or developer tools are described as ideal experience areas.
- Experience building and scaling production AI agents using frameworks such as Claude Code, Codex, or LangGraph.
- Deep Kubernetes expertise, including pod orchestration, persistent storage, RBAC, and multi-cluster management.
- Strong Python skills and production API experience using FastAPI, Flask, or similar async frameworks.
- Distributed-systems experience with Kafka, Redis, and MongoDB or PostgreSQL.
- Experience building and managing GitLab CI and ArgoCD pipelines for continuous delivery to Kubernetes.
- Experience designing AI data platform components and developer-facing platform APIs used by multiple engineering teams.
- Knowledge of OAuth 2.0, JWT, token exchange, and Vault secrets management.
Preferred
- Experience building or operating AI agent platforms or agentic workflow systems, with hands-on expertise in agent protocols and...
- Hands-on experience with RAG architectures, embedding pipelines, and vector databases such as Milvus, Pinecone, or Weaviate.
- Full-stack skills with React or Vue for developer portals and dashboards.
- Contributions to open-source infrastructure or platform tooling.
Original job description
Content provided by the employer
Original job description
Content provided by the employer
We are looking for a Sr. Engineer to design, build, and scale the infrastructure powering NVIDIA’s AI agent ecosystem. You will work at the intersection of distributed systems, developer platforms, and agentic AI — building the foundational services that enable teams across the company to develop, deploy, orchestrate, and operate autonomous AI agents at production scale.
What you will be doing:
- Build and develop platform services that manage the full agent lifecycle from registration through deployment, execution, and teardown
- Architect multi-tenant Kubernetes execution environments with pod lifecycle management, per-tenant namespace isolation, persistent storage, admission policy enforcement, and identity propagation
- Develop and maintain automated CI/CD pipelines using GitLab CI and ArgoCD, including reusable pipeline templates and deployment blueprints that standardize how agents are built across teams
- Build framework-agnostic infrastructure supporting multiple agent SDKs and harnesses — lifecycle hooks, skills configuration, memory services, and OTEL instrumentation — so teams can onboard new agent frameworks without platform changes
- Build and operate Kafka-based message pipelines and real-time event streaming using Redis PubSub and SSE
- Develop data ingestion pipelines, access interfaces, and storage layers that power AI agent knowledge and context
- Implement session management for state persistence, conversation history, and agent recovery across sessions
- Develop multi-layer auth using OAuth 2.0, JWT validation, token exchange, and gateway integration, and manage secrets lifecycle with Vault (provisioning, rotation, container injection)
- Partner with security teams on compliance, access controls, and approval workflows for agent operations
What we need to see:
- Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience), with 12+ years in software engineering — ideally in platform engineering, infrastructure, or developer tools
- Experience building and scaling AI agents in production using frameworks like Claude Code, Codex, or LangGraph
- Deep Kubernetes expertise including pod orchestration, persistent storage, RBAC, and multi-cluster management
- Strong Python skills with production API experience using FastAPI, Flask, or similar async frameworks
- Proven track record designing distributed systems with Kafka, Redis, and MongoDB or PostgreSQL
- Expertise building and managing robust CI/CD pipelines using GitLab CI and ArgoCD for continuous delivery to Kubernetes
- Experience designing AI data platform components (ingestion pipelines, vector stores, retrieval APIs, data preprocessing workflows) and building developer-facing platform APIs consumed by multiple engineering teams
- Solid grasp of auth and identity: OAuth 2.0, JWT, token exchange, and secrets management with Vault
- History of leading sophisticated technical projects such as migrations or greenfield platform builds, with strong interpersonal skills to drive alignment across teams and write clear design documents
Ways to stand out from the crowd:
- Experience building or operating AI agent platforms or agentic workflow systems, with hands-on expertise in agent protocols and frameworks like MCP, A2A, LangChain, or LangGraph
- Hands-on experience with RAG architectures, embedding pipelines, and vector databases (Milvus, Pinecone, or Weaviate)
- Full-stack skills with React or Vue for building developer portals and dashboards
- Contributions to open-source infrastructure or platform tooling
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.About the company
NVIDIA
Large Enterprise
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.