Top Artificial Intelligence Jobs in Jerusalem

The Head of Digital Acquisition will lead HiBob's global paid growth engine, optimize paid media campaigns, mentor a team, and drive pipeline growth across segments.

The GTM AI Product Manager will execute GTM strategies into operational AI solutions, manage project scopes, and ensure successful implementations across teams, while building prototypes and facilitating stakeholder partnerships.

Lead technical direction for HiBob's AI domain: design scalable AI and agentic architectures, own cross-team initiatives, drive decisions around LLMs, retrieval, tool calling, MCP and evaluation frameworks, mentor engineers, and ensure production-grade reliability and scalability.

As a Senior Product Manager, you will define and evolve GitLab's AI Platform Management capabilities, focusing on AI Control Plane and strategic governance. You'll lead product discovery, drive stakeholder alignment, and collaborate across teams to deliver effective management solutions for AI usage.

Partner with Customer Success to identify and deliver AI use cases: build, test, and operationalize AI agents, automations, and workflows integrated with CS systems (Zendesk, Slack, Notion, CRM). Drive adoption through enablement, define KPIs, measure impact, and translate field intelligence into product and process improvements to move CS from reactive support to proactive operational intelligence.

The role involves managing the AI platform roadmap, defining reusable AI capabilities, collaborating with engineering, and shaping AI standards within the product ecosystem.

Lead a team of engineers to design and implement scalable systems for AI-driven experiences, ensuring integration across the platform, while fostering a culture of innovation and growth.

Design, build, and maintain backend infrastructure and microservices enabling AI-driven features. Integrate AI models, develop APIs and data pipelines, ensure performance and resilience, drive engineering excellence, and mentor other backend developers.

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.

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 and deliver AI-driven engineering initiatives from scoping and architecture through hands-on execution. Drive adoption of AI tooling and practices across Dev and DevOps, identify opportunities to improve velocity, incident response, cost efficiency and reliability, align stakeholders, unblock dependencies, and evaluate emerging AI tools for production-grade solutions.

Lead AI adoption across R&D by building agentic tooling, creating shared context and prompts, developing internal agents and integrations, teaching engineers, measuring impact, and defining safe, compliant AI usage while contributing to AppWork's AI framework.

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.

Design and build reference architectures, standards, and shared infrastructure for agentic AI across the company. Implement evaluation harnesses, monitoring, registries, and cross-company automations. Conduct technical reviews, mentor citizen developers, support governance and risk controls, and drive platform/vendor selection and technical thought leadership.

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.

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.

The AI Operations Engineer will automate workflows, build AI agents, and integrate tech solutions to improve operational efficiency in healthcare at Droxi.

Research and develop generative and multimodal AI solutions for image and video; train, fine-tune, evaluate, and optimize large models; prototype and productionize scalable model-serving pipelines; collaborate with engineering and product teams to deliver AI-driven media capabilities at scale.

Lead end-to-end product for an agentic natural-language-to-quantum-program experience: define roadmap, plugin/dev-tools integration, internal harness, eval metrics, user discovery, and cross-functional delivery sequencing.

The AI Architect will lead AI architectural guidance, hands-on innovation, and establish best practices for AI development while ensuring compliance with regulations.

The Engineering Squad Lead will lead and build systems, make architectural decisions, and integrate AI into team workflows while improving engineering practices.

Develop and maintain Agentic AI components including multi-agent orchestration and RAG workflows. Implement backend logic and data processing in Python and SQL, integrate systems via APIs, write specs and tests, support debugging and production issues, collaborate with global teams, engage end users, and document solutions.

Record short video and audio clips and perform short scripts in varied tones and emotions. Transcribe/capture text from audio/video. Work remotely on a project basis with flexible hours; no guaranteed minimum.