Top Artificial Intelligence Jobs in Jerusalem

Own strategy, discovery, roadmap, execution, compliance, integrations, and go-to-market activities for a digital advertising product. Partner with customers, engineering, design, data, sales, marketing, and customer success teams to define requirements, launch features, measure outcomes, and iterate. The role requires B2B SaaS product management experience, analytical strength, AI fluency, strong communication, and comfort working in ambiguity.

Lead and scale an Eastern-region channel sales organization, including recruiting, coaching, forecasting, pipeline management, quota attainment, and enterprise customer development. Build relationships with CISOs and security leaders while partnering with marketing, product, and sales operations on go-to-market execution. Manage channel relationships across VARs, MSSPs, technology alliances, cloud alliances, and systems integrators, while establishing scalable sales processes and conveying field intelligence to the broader organization.

Design, build, and maintain backend infrastructure and microservices for AI-driven features. Integrate AI models and services with core systems, develop APIs and data pipelines, and optimize real-time processing for performance, scalability, and resilience. Lead feature development from design through deployment, promote testing and automation, participate in architecture discussions and code reviews, and mentor backend developers.

Leads end-to-end security research for AI agents within Microsoft Project Perception, defining investigation problems, research methodologies, evaluations, production readiness, and customer-facing outcomes. Partners with engineering, data science, and product teams; analyzes large-scale security data; applies AI and agentic tooling to improve detection and investigation; influences strategy and roadmaps; and mentors researchers.

Lead strategy, roadmap, and execution for a high-impact product domain involving financial logic, technical architecture, and AI capabilities. Align executive and cross-functional stakeholders, define KPIs, manage product maturity through production, and develop detailed specifications. The role requires deep analysis of financial calculations, business logic, APIs, schemas, and data models, along with hands-on AI prototyping and technical collaboration with engineering.

Designs data architecture, pipelines, governance, and quality standards while developing and deploying production ML models. Owns the full model lifecycle from research and experimentation through monitoring and iteration. Partners with Product and R&D to deliver AI capabilities for vulnerability detection, attack emulation, and security insights, while evaluating build, buy, and fine-tuning options.

Lead Guesty’s company-wide AI transformation by owning the roadmap, AI team, infrastructure, governance, tooling budget, and cross-department automation initiatives. Drive measurable improvements in hours saved, workload automation, cost avoidance, and revenue impact. Lead AI transformation managers and engineers, enable business teams to build safe automations and agent workflows, evaluate emerging technologies, report to senior leadership, and remain hands-on enough to guide implementation and unblock delivery.

Conduct advanced security research across operating systems, cloud environments, enterprise software, APIs, microservices, and containers. Discover vulnerabilities, reverse-engineer software, develop proof-of-concept tools, use AI to accelerate research, publish findings through blogs and conferences, and collaborate with detection and product teams to turn discoveries into security capabilities.

Own production inference for large language models across single and multi-node GPU systems. Select and tune precision, quantization, parallelism, memory strategies, batching, concurrency, topology, and serving runtimes. Benchmark and validate latency, throughput, utilization, and cost; operate model lifecycles and resolve incidents across runtimes, GPUs, hosts, drivers, and networks. Build software that automates deployment decisions and captures inference expertise for reliable multi-tenant and dedicated serving.

Leads FP&A for the global Academia & Government segment, overseeing P&L reporting, forecasting, planning, variance analysis, revenue and customer metrics, financial modeling, and executive decision support. Partners across Sales, Operations, Data, HR, Accounting, and other functions to improve reporting and performance management. Prepares executive and board materials, supports strategic investments and transformation initiatives, and leads, coaches, and develops the FP&A team.

Own the vision, strategy, roadmap, and end-to-end development of AI-native agreement experiences for SignNow. Lead customer discovery, product validation, prototyping, technical trade-off evaluation, launch, adoption, and continuous improvement. Partner with engineering, design, data, and go-to-market teams to build reliable, transparent agentic workflows, define success metrics, prioritize investments, and deliver measurable customer and business impact.

Leads LiveU’s enterprise Data, Analytics, and AI roadmap by building a scalable cloud data platform, deploying production AI and ML solutions, enabling company-wide GenAI adoption, and delivering self-service BI. Oversees data governance, security, compliance, FinOps, systems integration, and executive reporting while managing and mentoring data and AI engineers and analysts.

Design and build scalable backend systems and production-grade AI solutions for cyber-physical security environments. Responsibilities include owning features from architecture through deployment, developing LLM applications, RAG systems, and AI agents, integrating AI-assisted engineering tools, and promoting resilient, clean software architecture across cloud and on-premise environments. The role requires strong Python backend development, distributed systems expertise, real-time data and networking experience, and hands-on AI application deployment.

Leads Cynet’s Product, Engineering, and Counter Adversary Operations organizations, owning company-wide technology and product strategy, AI adoption, product quality, release predictability, cybersecurity platform integration, and customer outcomes. Serves as Managing Director of the Tel Aviv office, overseeing site stability, culture, talent attraction, employee experience, and cross-team leadership. Partners with executive leadership to align technology and product strategy with commercial goals.

Build and own AI agent-powered content creation features across the stack using Node.js, TypeScript, and React. Develop agents that process audio, video, and text to create and edit professional content, while shaping user experiences for reviewing and applying agent outputs. Collaborate closely with Product, manage dependencies, work through ambiguity, and use AI tools for development and debugging.

Build and deploy practical AI solutions, including AI agents, agentic workflows, LLM integrations, and business automations. Partner with cross-functional teams to identify opportunities, evaluate AI platforms, architect enterprise integrations, optimize prompts, and lead initiatives from prototyping through production. Ensure solutions are scalable, secure, governed, and aligned with business outcomes while communicating effectively with technical and business stakeholders.

Serve as the senior ML and AI technical expert for customers and field engineering. Architect and optimize production-grade ML, GenAI, agentic, RAG, MLOps, and data engineering solutions on Databricks. Support technical sales, mentor solution architects, communicate complex concepts, guide model deployment and monitoring, and influence product strategy through customer feedback.

Build and operate production AI agents, agentic workflows, and AI-enabled developer tools. Design AI systems using agent frameworks, RAG, MCP, tool calling, and guardrails. Develop AI SDLC platforms, observability, tracing, evaluations, and cost analysis. Maintain infrastructure, CI/CD, GitOps workflows, and security controls for AI services. Create internal developer tooling and automation while partnering with engineering and product teams on scalable AI solutions.

Build and operate Guesty’s AI platform, agent runtimes, developer tooling, cloud infrastructure, and production systems. Design scalable event-driven architectures, write TypeScript and Python backend code, manage containers and CI/CD, and implement observability through logging, tracing, and metrics. Lead initiatives from research and architecture through deployment and monitoring while enabling more than 100 engineers to develop reliable AI applications.

Research how adversaries exploit generative AI, LLMs, autonomous agents, on-device assistants, and multimodal systems. Conduct hands-on adversarial prompting and jailbreak research, develop tooling, translate findings into defensive product improvements, and publish security research through blogs and conference talks. Serve as a subject matter expert in adversary tradecraft and security operations while collaborating with researchers and business units.

Lead complex, cross-functional technology programs across AI, software, robotics, vehicle hardware, infrastructure, simulation, data, and product teams. Own project delivery, improve efficiency, coordinate stakeholders, manage risks and dependencies, align programs with business goals, and drive continuous improvement. The role requires strong technical judgment, systems thinking, communication, influence, and experience delivering complex software or technology programs in fast-paced environments.

The Head of AI & Automation will lead MAËLYS' AI transformation by defining strategy, building AI solutions, and driving business impact across functions.

Own the AI platform roadmap and develop reusable capabilities such as retrieval, embeddings, scoring, and agent tooling. Partner with engineering, data, and product teams on architecture, governance, evaluation, monitoring, safety, cost, and transparency. Enable adoption of AI capabilities across products, improve developer experience, and establish standards for building reliable AI systems in production.

Lead the technical direction of an AI platform domain, designing scalable architectures and production-ready agentic systems. Drive decisions involving LLMs, retrieval, tool calling, MCP, evaluation frameworks, and AI infrastructure. Own cross-team initiatives from design through production, establish engineering standards, mentor teams, conduct architecture reviews, evaluate emerging technologies, and balance rapid experimentation with reliability, scalability, and production quality.

Own end-to-end data science for fraud and risk detection products, including LLM and classical ML modeling, prompt engineering, evaluation, experimentation, monitoring, and production deployment. Partner with engineers on real-time streaming and batch systems while defining data-quality metrics, observability, labeling strategies, and performance improvements for adversarial, imperfectly labeled data.