Level

NVIDIAPosted 1d ago

L3

Software Solution Architect, NVIS

Software Solution Architect, NVIS at NVIDIA scores 75 out of 100 on AI centrality, which makes it a Level 3 role on this board.

Israel, Tel AvivmidFull time

AI in this role

Design and build production-grade agentic AI solutions, tools, and LLM-powered applications for NVIDIA's delivery organization.

python
ragai-agentsai-safetyllmagentic-workflowssoftware-architecturebackend-development

As a Software Solution Architect, NVIS at NVIDIA, you will lead the transformation of AI infrastructure. Our NVIS team focuses on developing the next generation of NVIS Central, an agentic software platform with tools, services, and AI agents that automate, simplify, and speed up the work of our delivery organization. This role offers an outstanding chance to create and build LLM-powered agents that improve execution visibility, cut down manual tasks, and standardize workflows. These efforts allow NVIS to grow quickly and with high quality. Join us to bring up, validate, optimize, and upgrade large-scale AI Factory infrastructure for some of the world’s most advanced accelerated computing environments!

What you'll be doing:

  • Compose, build, and productionize agentic AI solutions, tools, and applications for the NVIS delivery organization.

  • Develop LLM-based agents, skills, tool-calling workflows, orchestration logic, backend services, APIs, data pipelines, and automation features as part of NVIS Central.

  • Translate field, delivery, operations, and product needs into clear technical builds, agent workflows, and working software.

  • Develop agents that can reason across project data, knowledge bases, operational systems, logs, reports, and delivery workflows.

  • Build workflows that help NVIS teams identify risks, summarize project status, automate repetitive tasks, improve readiness visibility, and simplify handoffs.

  • Work with timely engineering, retrieval-augmented generation, context management, agent memory, function calling, evaluations, and guardrails to build reliable AI systems.

  • Integrate LLMs and agents with internal systems, project data sources, knowledge repositories, reporting tools, and operational workflows.

  • Collaborate closely with software developers, architects, product managers, DevOps/SRE, and NVIS field teams to successfully implement reliable and scalable solutions.

  • Contribute to engineering guidelines, including code quality, testing, CI/CD, observability, documentation, security, and production support.

What we need to see:

  • B.Sc. degree or equivalent experience in Computer Science, Computer Engineering, or a related technical field.

  • 2+ years of hands-on software development experience building production applications, platforms, automation tools, or AI-based systems.

  • Strong programming experience with Python and modern backend development.

  • Hands-on experience working with LLMs, agentic workflows, timely composition, tool/function calling, RAG, and AI application development.

  • Experience crafting and implementing RESTful APIs, data services, workflow automation, and integrations with enterprise systems.

  • Experience building reliable software around non-deterministic AI systems, including testing, evaluation, monitoring, and failure handling.

  • Experience with Docker, Kubernetes, CI/CD, Git, observability, and cloud-native development practices.

  • Background with SQL and NoSQL databases, data modeling, querying, indexing, and data integration.

  • Excellent problem-solving skills, ownership attitude, and ability to operate in a fast paced, cross-functional environment.

Ways to stand out from the crowd:

  • Experience building agent platforms, copilots, multi-agent systems, tool-calling workflows, evaluation frameworks, or MCP-style integrations.

  • Deep understanding of LLM application patterns such as context engineering, retrieval quality, timely/version management, agent planning, human-in-the-loop workflows, and AI safety guardrails.

  • Experience with AI infrastructure, HPC clusters, NVIDIA DGX systems, SuperPOD, Spectrum-X, Ethernet, InfiniBand, Kubernetes, or SLURM.

  • Experience in automating workflows related to field, delivery, operations, or professional services.

  • Strong Linux, networking, security, SRE, or distributed systems background.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RagAI AgentsAI SafetyLlmAgentic WorkflowsSoftware ArchitectureBackend DevelopmentPython

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. How do you think about the risk of an AI system in this kind of role failing silently?
  4. Tell me about a project where llm was part of your work. What did you do?
  5. Tell me about a project where agentic workflows was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Rag, AI Agents, AI Safety, Llm, and Agentic Workflows. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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