Top Artificial Intelligence Jobs in Jerusalem

Drive AI and business transformation initiatives from strategy through execution. Manage governance, prioritization, workstreams, dependencies, risks, KPIs, ROI models, and stakeholder accountability across global teams. Partner with business, product, AI, and IBT stakeholders to implement adoption frameworks, build transformation processes, resolve blockers, and communicate progress to senior leadership.

Leads Data Science, Network Research, and Asset Classification teams developing highly accurate cyber-physical system visibility solutions. Oversees AI/ML scaling, feature engineering, industrial and medical protocol parsing, data fusion, and passive and active data collection. Collaborates with product and R&D teams to support asset discovery, classification, risk management, segmentation, and threat detection across OT, ICS, and IoMT environments.

Own the AI cost visibility product end-to-end, building Go backend services, APIs, and TypeScript/React interfaces. Translate cloud AI spend data into clear FinOps experiences, use AI throughout development, and ensure reliability, monitoring, and measurable customer impact. Define product direction, work through ambiguity, contribute to architecture and design reviews, mentor engineers, and deliver scalable multi-tenant SaaS capabilities across public cloud platforms.

Design and develop production-grade generative AI applications and agentic systems. Build secure, scalable, observable AI services using Python, Docker, and cloud technologies, while applying testing, evaluation, CI/CD, and troubleshooting best practices. Collaborate with researchers to transform prototypes into reliable containerized cloud applications. Contribute to technical innovation, product development, and engineering standards across GenAI solutions.

Own the end-to-end architecture of Kaltura’s agentic platform, integrating speech recognition, retrieval, planning, tool calling, speech synthesis, avatar rendering, and video systems. Establish platform standards, interfaces, governance, latency and cost budgets, privacy models, observability, and capability integration strategies. Prototype solutions, evaluate architectural options, and partner with research, engineering, product, executives, and customers to deliver scalable, reliable, multi-tenant production systems.

Build the shared platform layer for Kaltura’s agentic AI systems, including orchestration, skills registries, evaluation, tracing, guardrails, memory, retrieval, and integration gateways. Develop production backend services in Python supporting real-time and offline video runtimes. Design scalable, multi-tenant platform primitives, integrate LLM and RAG infrastructure, optimize for latency, and improve reliability, observability, safety, and customer extensibility.

Develop scalable full-stack web applications with a strong frontend focus using Angular and .NET. Build microservices, APIs, event-driven integrations, and Azure cloud-native solutions with MongoDB, Redis, and Azure Service Bus. Extend the GrandNode-based sourcing platform, integrate AI-powered features, optimize performance and reliability, and address concurrency and distributed locking. Collaborate across Product, R&D, Operations, and other teams while contributing to architecture, code reviews, troubleshooting, and engineering improvements.

Own Pentera’s internal AI transformation strategy and build production AI agents, context layers, retrieval systems, action tools, and evaluation and safety infrastructure. Set architecture, model, vendor, ROI, security, and quality standards while partnering with business teams to scale successful workflows. Mentor AI Champions, advise leadership, and establish reusable internal AI platform patterns.

Leads the design, development, deployment, and scaling of generative AI platforms and systems. Responsibilities include building Python-based microservices, applying LLM, RAG, fine-tuning, and inference techniques, designing secure APIs, implementing DevOps practices, collaborating with product and business teams, solving complex technical problems, and mentoring engineers.

Own cloud features end to end, including architecture, backend services, React interfaces, database design, testing, CI/CD deployment, monitoring, troubleshooting, and production support. Use AI coding tools such as Claude Code, Cursor, and GitHub Copilot to accelerate development. Collaborate with Product and UX teams to deliver scalable, reliable customer experiences on public cloud platforms.

Build and deploy production-grade LLM applications, RAG systems, agentic workflows, and internal AI tools. Evaluate foundation models, develop tool-using and multi-agent systems, improve reliability and reduce hallucinations, and establish evaluation methodologies for quality and safety. Collaborate with algorithms, mapping, software, and research teams to transition prototypes into scalable products.

Own the Data and AI product domain across the full lifecycle. Define product strategy, AI model integration and behavior, data analytics capabilities, and the roadmap for cybersecurity products. Partner with R&D, Sales, and customers to gather market insights, deliver AI/ML-driven features, and drive product adoption, revenue, and ongoing success.

Leads the HR Technology Center of Excellence and advises senior leaders on HRIS strategy, governance, risk, controls, and technology roadmaps. Owns complex issues across Workday, payroll, and downstream systems while driving AI, analytics, process improvement, data integrity, compliance, and global operating standards. Partners with HR, IT, Finance, business leaders, and vendors to implement scalable technology-enabled transformations across North America, Europe, and Asia.

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.

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.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

The engineer will optimize AI libraries and components for unique hardware, focusing on performance improvements in AI models and kernels.

The AI Compiler Engineer will develop the AI graph compiler and work on the software stack to optimize AI models for unique hardware architecture. Responsibilities include enhancing performance for AI models and contributing to related components in the software stack.

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 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 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.

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.