Top Artificial Intelligence Jobs in Israel

Manage and mentor a software engineering team building automation, provisioning, configuration, and monitoring platforms for HPC/AI data centers. Drive technical direction, collaborate with architects and DevOps, lead code reviews and design discussions, troubleshoot distributed systems, and improve observability and reliability.

The AI Researcher will research advanced AI model capabilities, develop assessment methodologies, create mitigation solutions, and support product development.

Technical owner for SysAid's Context Lake and Agentic Layer: build and maintain GitHub-based structured context, design agent workflows and orchestration, integrate with martech (CRM, automation, paid media), enable marketers to run automated campaigns, and iterate infrastructure and tooling to improve agent outputs and production reliability.

Lead Windward's company-wide AI transformation across adoption, infrastructure, and builds. Build and run the program end-to-end, manage and grow teams, drive change with VPs, prioritize initiatives, create and sometimes implement agents and workflows, and measure/report impact (hours saved, adoption, platform leverage).

Design, prototype, and deliver AI-powered solutions using agentic and low-code platforms. Lead technical workshops, build agentic workflows and CI/CD for an AI builders community, ensure responsible AI practices, and collaborate with business and IT teams to deploy secure, maintainable solutions.

The Senior AI Engineer will build modular AI capabilities and autonomous agents for the CYE Platform, ensuring reliability and usability while collaborating with non-technical users.

Drive go-to-market strategy for AI products, conduct market research, develop product messaging and positioning, and enable sales teams for success.

Lead definition and hands‑on creation of AI‑native customer experience solutions. Own AI capabilities across the CX platform, design and prototype AI product experiences, validate with customers, drive AI‑first strategy, collaborate cross‑functionally, and deliver enterprise‑ready roadmaps focused on model performance, analytics, and security/governance.

Partner with R&D to identify AI opportunities, design and ship LLM-powered tools and agentic systems, build shared AI infrastructure and developer-facing tooling, evaluate models and new AI tech, establish guardrails and monitoring, ensure security/compliance, and take solutions from prototype to production with documentation and runbooks.

Lead AI engineering strategy and execution across the company: prioritize and coordinate GenAI and agentic initiatives, set technical direction for prompts, retrieval, serving, observability, and guardrails, partner with platform engineering to scale infrastructure, build governance and education programs, and translate AI roadmaps into measurable business outcomes.

Design and refine prompts, build automations integrating LLMs, write Python/SQL code, perform QA on AI models, research AI advances, and collaborate with cross-functional teams to deliver AI-driven products.

As an AI Data Engineer, you will design and implement data pipelines for the Large Market Model (LMM), focusing on feature engineering, system reliability, and driving innovation through advanced AI technologies.

The AI Data Engineer will architect and engineer data pipelines for the Large Market Model (LMM), ensuring system reliability and exploring innovative features in AI-driven solutions.

The Algorithms Engineer will develop AI and Computer Vision solutions, manage model training and evaluation, and collaborate on product integration.

The R&D Manager for AI Python Frameworks will lead a team to design and build foundational AI infrastructure, focusing on agent lifecycle management, observability, and developer enablement, while ensuring AI system reliability and quality across the organization.

Lead development of AI-driven verification tooling: build AI ecosystems, integrate ML/LLM agents into verification flows, automate stimulus generation and coverage closure, create intelligent debug assistants, research AI approaches to state-space explosion, and train teams on human-in-the-loop methodologies.

Develop and deploy agentic systems integrating LLMs with domain tools, enhance NVIDIA products through innovative AI flows, and improve engineering workflows.

The AI Developer role involves designing and integrating AI agents, developing full-stack features, maintaining backend services, and continuous experimentation.

Design, build, and deploy LLM-powered autonomous agents and production AI infrastructure. Own end-to-end lifecycle from prototype to production, including evaluation, observability, scaling, and cross-functional delivery to accelerate engineering workflows.

Lead development and execution of AI-driven AML products. Define strategy, roadmap, and requirements; work with engineering, data science, and design; engage customers and stakeholders; apply ML and analytics to improve detection; track KPIs and iterate on product performance.

Lead product strategy and delivery for internal AI and productivity tools in Israel, represent local stakeholders on the global roadmap, partner with engineering to design and ship full-stack integrations (NVBugs, Jira, Salesforce, Git systems), and define adoption and productivity metrics across NVIDIA.

Lead strategy, roadmap, and delivery of ML products for global supply chain. Partner with data science, engineering, and business to take models from concept to production with strong MLOps, data readiness, governance, and measurable business impact. Build practice, mentor teams, and operationalize Responsible AI and value tracking.

Lead strategy, roadmap, and delivery of agentic AI products for supply chain. Design multi-agent systems, ensure data readiness, establish Responsible AI governance, build practice and playbooks, mentor teams, prioritize by ROI, and drive adoption and measurable business value across enterprise supply chain operations.

Lead strategy, roadmap, and delivery of generative AI products (LLM copilots, knowledge assistants, document intelligence) for supply chain. Drive data readiness, governance, evaluation, cost management, and value tracking. Build practice, mentor teams, and align cross-functional partners to scale production LLM solutions with Responsible AI controls and measurable business outcomes.

Design, build, deploy, and maintain backend systems integrating Generative AI. Implement APIs, databases, authentication, multi-agent orchestration with LLMs, optimize performance and cost, deploy on cloud (GCP/Azure/AWS), and collaborate with clients and DevOps on delivery.