Top Artificial Intelligence Jobs in Jerusalem

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.

Develop backend services and cyber defense solutions for Microsoft Security. Responsibilities include improving service lifecycles, analyzing complex system behavior, streamlining deployments, reducing costs, collaborating across engineering and security teams, and using AI-assisted development tools. The role requires hands-on backend software development experience, initiative, and the ability to design and operate scalable services in cloud or on-premises environments.

Own a portfolio of conversational AI products from opportunity identification through launch, scaling, and iteration. Define success metrics, monitor performance, improve conversational quality, and manage hallucination and renter feedback metrics. Translate partner, operations, and user feedback into roadmaps, PRDs, and Jira tickets. Collaborate closely with Engineering and Data Science on experiments, labeling, evaluation loops, engine improvements, and new AI capabilities. The role requires hands-on execution, deep product expertise, and comfort with ambiguity.

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.

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 architecture discussions, code reviews, testing, deployment automation, and engineering best practices while mentoring backend developers. This is a nine-month temporary maternity-leave position.

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.

Own global paid acquisition strategy and execution across Google Ads, LinkedIn Ads, and other channels. Manage multi-region budgets to drive pipeline, revenue, and ROI; partner with cross-functional teams; integrate AI into campaign optimization, targeting, reporting, and workflow automation; and lead experimentation across campaigns, audiences, creatives, and landing pages.

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.

Develop embedded software for computer vision components within Innoviz’s LiDAR-based embedded solutions. Responsibilities include implementing computer vision software, defining embedded constraints and implementation details for algorithm teams, and debugging advanced object detection and classification systems used in automotive and autonomous driving applications.

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.

Explores and prototypes AI, agent, real-time transport, memory, and infrastructure technologies. Runs technical spikes, evaluates unfamiliar codebases, designs benchmarks, measures latency and cost, and produces evidence-based build-versus-adopt recommendations. Hands findings to production teams and tracks the evolving AI tooling landscape.

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.

Lead and manage all global legal and compliance functions, advising executives on commercial, M&A, contracts, AI/privacy, and regulatory risk. Oversee legal governance, statutory filings, policies, and a global legal team while supporting growth, partnerships, and cross-border operations.

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.

Leads development of AI optimization software across Microsoft AI hardware platforms. Designs and implements AI kernels, optimization algorithms, compilation techniques, and runtime improvements to increase performance, scalability, and efficiency. Architects complex solutions, writes and reviews C++ and Python code, drives testing and automation, collaborates across model, systems, compiler, and hardware teams, and mentors engineers while ensuring security, reliability, and maintainability.

Leads development and optimization of AI software frameworks across Microsoft accelerator hardware. Designs AI kernels, optimization algorithms, compilation techniques, runtime and serving improvements, and distributed execution solutions. Provides architectural and technical leadership, mentors engineers, drives testing, automation, deployment, reliability, telemetry, incident response, and cross-team delivery. Requires strong C++ and Python coding experience, advanced AI optimization expertise, and deep knowledge of performance, scalability, security, and production operations.

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.

Build and deploy AI-powered tools, autonomous agents, data pipelines, and workflow automations for Sales and Marketing teams. Translate go-to-market strategies into production-grade internal systems, integrate modern APIs and platforms, and improve revenue operations efficiency. Partner with Finance, Sales, and Marketing stakeholders to ensure solutions are secure, scalable, measurable, and deliver strong conversion and productivity gains.

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.

Own AI deployment methodologies, standards, governance, and best practices across the organization. Guide teams designing and implementing AI solutions, coordinate cross-functional stakeholders, support project execution, manage risks and dependencies, and develop playbooks, architectures, templates, training, and knowledge-sharing programs. Ensure AI implementations are consistent, scalable, reliable, and production-ready while enabling teams to apply modern approaches including RAG, prompting, agents, and guardrails.

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.

Owns the roadmap and product lifecycle for AI usage control within an enterprise cybersecurity platform. Responsibilities include engaging CISOs and security teams, conducting market and competitive research, analyzing product metrics and feedback, defining and prioritizing features, managing the backlog, and collaborating with engineering, design, marketing, sales, and customer success through launch and adoption.