Software Solution Architect

NVIDIA

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

Posted 9/23/2026full-timeTel Aviv • IsraelJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing LLM-based agents and workflows, integrating AI solutions with internal systems, and building reliable software for non-deterministic AI systems. Proficient in automation, RESTful API development, and cloud-native practices, with a strong foundation in programming and problem-solving.

Highest-signal resume keywords

Python ProgrammingLLM Application DevelopmentRESTful API DevelopmentDocker and KubernetesWorkflow Automation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software DevelopmentAI Application DevelopmentData IntegrationSQL and NoSQL DatabasesData ModelingTesting and EvaluationObservabilityCloud-Native DevelopmentAgent WorkflowsFunction Calling

Soft Skills

Problem-SolvingOwnership AttitudeCollaboration

Tools & Technologies

CI/CDGitHPC ClustersNVIDIA DGX SystemsSuperPODSpectrum-XEthernetInfiniBandSLURM

Industry Keywords

Agentic AI SolutionsAutomation ToolsOperational WorkflowsCross-Functional EnvironmentAI Safety Guardrails

Tech Stack

Tools & technologies

CloudDistributed SystemsDockerKubernetesLinuxNoSQLPythonSQL

About the role

Key responsibilities & impact

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 reason across project data, knowledge bases, operational systems, logs, reports, and delivery workflows
Build workflows to 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 with software developers, architects, product managers, DevOps/SRE, and NVIS field teams to implement reliable and scalable solutions
Contribute to engineering guidelines covering code quality, testing, CI/CD, observability, documentation, security, and production support

Requirements

What you’ll need

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
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