Top Artificial Intelligence Jobs in Jerusalem

Support sales by delivering technical demos, POCs, and workshops; translate customer needs into solutions; design integrations, automation, and playbooks; respond to procurement requests; collaborate with sales, product, and support throughout the pre-sales lifecycle.

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.

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.

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

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 and govern AI-enabled business transformation initiatives, translating strategy into execution plans, KPIs, ROI models, and cross-functional adoption. Manage prioritization, dependencies, risk tracking, stakeholder alignment, and senior leadership communications to ensure measurable business impact.

Build, deploy, and optimize production LLM-powered healthcare applications. Design agentic workflows and multi-modal data pipelines, implement MLOps/LLMOps and CI/CD, ensure HIPAA compliance, profile performance, and collaborate with clinicians to deliver clinical decision support and document-processing solutions.

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 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.

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.

Build and prototype AI-driven cybersecurity product features, develop AI/data infrastructure and pipelines, analyze product and security data, define KPIs and alerts, and provide insights to Product, R&D, and GTM teams.

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.

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

The Applied AI Engineer will develop AI features, enhance model performance, debug issues across the tech stack, and collaborate with teams to deliver robust ML solutions.

The Full Stack Engineer will develop AI-driven workflows, integrate LLMs, and improve system reliability in collaboration with cross-functional teams.