Top Artificial Intelligence Jobs in Israel

Design and build an AI-native, production-grade full-stack system from scratch. Own features end-to-end, integrate LLMs, RAG pipelines, agents and ML models into production, ensure performance, reliability, guardrails and observability, and improve team platforms, patterns, and tooling.

Lead design, training, and deployment of deep learning computer vision models (detection, segmentation, tracking). Build real-time edge/cloud solutions, integrate SOTA research, monitor and improve production ML systems, collaborate across teams and mentor engineers.

Lead identification, design, and implementation of scalable generative-AI solutions. Evaluate models and tools, build prompts, RAG pipelines and AI agents, integrate workflows with systems, run projects from ideation to rollout, and guide organizational AI adoption.

Develop, train, evaluate, and deploy detection models for AI security. Translate threat research into production-ready models across ML, deep learning, LLMs, and agentic systems. Build benchmarks, labeled datasets, monitor production performance, and iterate with security researchers to ensure reliability and measurable impact.

The Senior Software Engineer will develop AI-focused software systems, build automation tools for data centers, and collaborate on system performance improvements.

Design, build, and deploy production-grade AI agents and agentic systems to automate multi-step business workflows. Own agent architecture, RAG pipelines, vector search, evaluation frameworks, and cross-functional delivery to measure and improve business impact.

Lead end-to-end QA for cyber-focused features: define and execute test plans (functional, integration, API, regression, exploratory), analyze logs, investigate defects, validate automation coverage, collaborate with developers and product, use AI tools to enhance testing, and contribute to QA documentation and release readiness.

Build AI-driven internal tools, automations, and workflows for Sales and Marketing. Own GTM tech stack, analyze revenue funnel, create dashboards, playbooks, AI agents, and integrate systems to improve conversion and efficiency.

Lead and build a quality engineering organization that owns automation, performance, and AI-driven quality. Define AI quality strategy, deploy LLM-powered test generation, architect scalable test frameworks, embed shift-left testing in CI/CD, run enterprise-scale performance validation, set KPIs/SLAs, and hire and mentor engineering and architect-level talent while partnering with Product and SRE.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Lead and build a Quality function for an AI-native R&D org: own in-Machine quality gates, production verification, monitoring-as-code, evals of LLM/agent output, set quality metrics, and manage a ~9-person team to ensure reliability and reduced regressions.

Develop AI models and algorithms for new products, analyze data, collaborate with engineers, and keep updated with AI trends.

The role involves evaluating AI outputs, generating original content, reviewing texts, and ensuring cultural relevance. Candidates should have native fluency in Hebrew and a strong command of English, with attention to detail and the ability to follow guidelines.

Lead a team to develop AI-driven products, manage engineering processes, analyze product data, and improve AI performance and outcomes.

Lead backend development and production deployment of LLM-based hiring agents: research LLMs, implement model-driven features, build evaluation frameworks, design scalable architecture, adopt CI/CD and cloud best practices, and mentor engineers.

Review, evaluate, and annotate AI-generated multilingual and multimodal content (text, images, audio, video). Perform quality checks, identify edge cases, contribute to dataset development, participate in calibration, and provide feedback to improve annotation guidelines and workflows.

The Agentic AI Engineer will develop AI-driven solutions, automate workflows, and collaborate with business teams to enhance processes while ensuring compliance and security.