4 days ago
Base Salary
$200k - $391k/yr
Responsibilities
- Build platform services managing AI agents from registration through deployment, execution, operation, and teardown.
- Architect multi-tenant Kubernetes execution environments with pod lifecycle management, namespace isolation, persistent storage, admission policies, and identity propagation.
- Develop reusable GitLab CI and ArgoCD pipelines, templates, and deployment blueprints for agent delivery.
- Create framework-agnostic infrastructure supporting agent SDKs, lifecycle hooks, skills configuration, memory services, and OpenTelemetry instrumentation.
- Build Kafka-based message pipelines and real-time event streaming with Redis PubSub and Server-Sent Events.
- Develop ingestion pipelines, access interfaces, storage layers, session management, state persistence, conversation history, and agent recovery capabilities.
- Implement OAuth 2.0, JWT validation, token exchange, gateway integration, and Vault-based secrets provisioning, rotation, and injection.
- Partner with security teams on compliance, access controls, approval workflows, migrations, and greenfield platform builds.
Requirements
- Bachelor's or master's degree in Computer Science, Engineering, or a related field, or equivalent experience.
- 12+ years of software engineering experience, ideally in platform engineering, infrastructure, or developer tools.
- Production experience building and scaling AI agents with frameworks such as Claude Code, Codex, or LangGraph.
- Deep Kubernetes expertise, including orchestration, persistent storage, RBAC, and multi-cluster management.
- Strong Python skills and production API experience with FastAPI, Flask, or similar asynchronous frameworks.
- Experience designing distributed systems with Kafka, Redis, and MongoDB or PostgreSQL.
- Expertise building CI/CD pipelines with GitLab CI and ArgoCD for Kubernetes delivery.
- Experience building AI data-platform components, ingestion pipelines, vector stores, retrieval APIs, data preprocessing workflows, and developer-facing platform APIs.
- Knowledge of OAuth 2.0, JWT, token exchange, identity, and Vault-based secrets management.
- Experience leading complex technical projects and driving cross-team alignment through clear design documentation.
- Preferred experience with AI agent platforms, MCP, A2A, LangChain, LangGraph, RAG architectures, embedding pipelines, Milvus, Pinecone, Weaviate, React, Vue, or open-source infrastructure and platform tooling.
Benefits
- Eligible for equity and benefits.
- Base salary is determined by location, experience, and compensation for similar positions.
- Applications will be accepted at least until October 20, 2026.
- This posting is for an existing vacancy.
Tech Stack
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.