The NVIDIA Networking Advanced Development Software group builds groundbreaking technologies that open new markets and deepen customer relationships. We focus on emerging areas at the intersection of networking and AI , including AI-driven development tools, high-performance networking for AI factories and data centers, and intelligent automation across the software lifecycle. Within this group, our team is building an AI engineering platform , a cross-domain, distributed and multi-disciplinary systems that enable AI agentic workflows to accelerate engineering development processes, on all stages of the development cycle. The platform combines multiple technologies within the industry standards, introducing new ways to handle complex tasks. Our technological stack based on K8s, AI harness agent, RAG, MCPs, and top tier LLMs. It is designed to drive the next generation of fully autonomous, long living agentic workflows, in scale and best performance in the industry. What you'll be doing: Design and build the core platform that powers an autonomous AI agent , including its reasoning engine, tool orchestration, and the runtime infrastructure it operates on Develop and evolve the Micro-services ecosystem that gives the agent its capabilities , from knowledge retrieval and log analysis to code execution and workflow automation Own features end-to-end: from requirements analysis and architecture, through implementation, to production deployment and iteration based on real usage Instrument, evaluate, and improve the platform's reliability , build observability, track quality, and feed signals back into the system to make the agent more effective over time Collaborate with engineering teams across the organization to identify high-impact workflows and translate them into AI-assisted automation that boosts developer productivity Work across the stack when the problem requires it , Python services, Kubernetes infrastructure, data stores, CI/CD pipelines, and developer-facing tools What we need to see: B.Sc. in Computer Science, Computer Engineering, or a related field 5+ years of relevant experience Solid system-level understanding with experience designing and delivering production services Ability to architect solutions, guide AI tools effectively, and reason about system behavior end-to-end Familiarity with containerization and orchestration (Docker, Kubernetes) Understanding of REST APIs, microservice architectures, and distributed systems Ability to learn complex concepts in a fast-paced environment Ways to stand out from the crowd: Familiarity with Kubernetes operators, Helm charts, and cluster management Experience with LLM application development , prompt engineering, agentic frameworks (ReAct, tool-use), or RAG pipelines Hands-on experience with FastAPI, async Python, or similar modern Python web frameworks Experience with vector databases, semantic search, or embedding models Knowledge of OAS (OpenAPI Specification), MCP (Model Context Protocol), and A2A (Agent-to-Agent) protocol ecosystem