Top Artificial Intelligence Jobs in Israel

Lead the end-to-end design vision for monday Agents, define new human-agent interaction paradigms, ship high-quality Desktop and Mobile experiences, raise design craft, mentor designers, and collaborate with Product, Engineering, Data Science, and Research to validate and iterate agentic features.

Build and operate ML- and LLM-powered systems for Stampli’s SaaS platform. Responsibilities include model training, evaluation, production integration, monitoring, scalable backend APIs, distributed systems, microservices, asynchronous processing, event-driven architecture, CI/CD, observability, and cloud infrastructure. The role partners with product and engineering stakeholders, leads technical design reviews, makes architectural tradeoffs, and delivers AI capabilities at scale.

Design and deliver production-ready enterprise AI architectures, translating customer business, data, security, and technical requirements into scalable solutions. Lead discovery, architecture workshops, integrations, model selection, RAG and agentic workflows, evaluation, observability, governance, and security. Provide technical leadership through implementation and production readiness, while building prototypes, validating decisions through code, and troubleshooting complex systems.

Lead customer deployments of Solid from kickoff through production, designing solution architectures and developing Python integrations and tooling. Build and validate semantic models, connect AI workflows and enterprise systems, troubleshoot technical issues, and serve as the primary technical customer contact. Manage multiple workstreams, collaborate with BI, IT, Data, Security, and business teams, and translate customer feedback into product requirements for R&D.

Develops internal AI-powered products and tools, evaluates and integrates AI development solutions, builds proof-of-concept projects, establishes prompt engineering practices, creates training materials, and advises leadership on AI adoption across engineering teams.

The AI System Architect leads and defines enterprise AI architecture, oversees integration strategies, manages platforms, and ensures governance compliance, shaping the AI roadmap and managing technical standards.

Design, deploy, and operate enterprise Voice AI, Text AI, conversational workflow, and conversation-analysis solutions. Translate customer requirements into technical implementations, integrate APIs, CRMs, telephony systems, databases, and knowledge bases, and configure prompts, workflows, models, and tools. Monitor production agents, troubleshoot integrations, optimize performance, and support SaaS customers while coordinating with Product, R&D, Sales, and Customer Success.

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.

Lead end-to-end data and automation solutions for Clinical Research: intake, business case development, design, build, validation, deployment and lifecycle management of ETL pipelines, dashboards, workflow automation and AI-assisted processes. Ensure compliance (ICH GCP), measure impact via KPIs, manage stakeholders and change, and scale reusable solutions across countries and region.

Own product positioning, messaging, launches, sales enablement, case studies, and client marketing for an AI-powered mobile gaming user-acquisition platform. The role translates complex technology into compelling buyer language, builds bottom-of-funnel proof, supports sales, develops customer community programs, and feeds market insight back to product and founders. Candidates must have product marketing experience in B2B SaaS, product launch experience, exceptional writing skills, and an AI-native workflow using Claude Code or a comparable agentic tool.

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.

Develops and manages end-to-end GenAI solutions, agents, automations, and digital workflows. The role translates business needs into scalable technology, leads continuous improvement initiatives, defines adoption and productivity KPIs, drives training and user adoption, and contributes to AI governance and standards. It also coordinates cross-functional stakeholders, resolves project complications, and supports software development and business process initiatives.

Designs scalable, high-performance LLM inference architectures across GPU clusters. Responsibilities include optimizing latency, throughput, cost, memory orchestration, scheduling, networking, KV caching, parallel execution, and dynamic batching. The role collaborates across software, hardware, compiler, model, and networking teams; evaluates emerging technologies; translates architecture into production systems; and authors technical specifications and documentation.

Lead end-to-end product design for an AI voice-agent platform: research, explore, and ship agent experiences. Bridge voice conversations and UI for configuration, review, handoff, and reporting. Use coding agents to move designs to production, partner with Product and Engineering, validate through research and metrics, and raise design craft across the team.

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.

Research and develop machine-learning-based physical-layer algorithms for 5G and future wireless products. Design, train, optimize, and benchmark neural networks for channel estimation, signal detection, beamforming, and decoding, targeting real-time embedded deployment. Responsibilities span research, simulation, Python/PyTorch/TensorFlow and MATLAB prototyping, specification, implementation support, integration, and customer release. The role evaluates ML approaches against traditional DSP methods for accuracy, latency, computational cost, and system performance.

Lead post-training evaluation, red-teaming, and reinforcement-learning gym audits for open-weight models. Build benchmarking and evaluation pipelines for LLM security threats such as indirect prompt injection, analyze reward diagnostics and rollout traces, audit agentic environments, produce evaluation cards, and integrate Dockerized environments into distributed training frameworks. The role requires expertise in RL training, AI security, PyTorch, containerization, verifiers, reward shaping, and agentic tool-use workflows.

Build and operate an enterprise AI agent harness, including CLI tooling, skills, tools, memory, guardrails, distributed services, MCP integrations, and evaluation pipelines. Develop reliable backend systems using Python and related technologies, integrate engineering data sources, instrument observability, troubleshoot agent behavior, and support platform adoption by teams in Israel and across distributed locations.

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.

Design and build scalable, secure cloud platform infrastructure for AI-driven SaaS cybersecurity products. Responsibilities include developing microservices, enabling CI/CD and DevOps processes, improving performance and reliability, collaborating across engineering teams, establishing backend engineering best practices, reviewing designs, mentoring engineers, and leading Agile projects involving high-scale, low-latency systems.

Lead large-scale business and technology transformation programs for public and private sector clients. Advise C-level executives on transformation strategy, operating models, organizational effectiveness, governance, and technology-enabled change. Own engagements from design through value realization, lead multidisciplinary teams, support business development, develop proposals, manage client relationships, and contribute to the growth of KPMG’s Technology Transformation practice.

Develop and productionize ML/AI models for sensitive data classification and insights. Own end-to-end lifecycle from problem framing, data strategy, model development, evaluation, deployment, and monitoring. Build deep learning and NLP solutions, agentic workflows, and rigorous evaluation pipelines for scalable, production-ready systems.

Build data infrastructure for AI research involving semiconductor chip design datasets. Responsibilities include collecting, cleaning, normalizing, sanitizing, parsing, and transforming structured and semi-structured data; developing Python and SQL pipelines; navigating codebases and filesystems; collaborating with researchers to create model-ready datasets; and documenting data sources, transformations, and dataset versions.

Own and evolve production-grade full-stack AI platform products end to end, including UX/UI, frontend, backend, APIs, data models, integrations, deployment, and operations. Lead adoption of agent-led development by evaluating models, coding agents, orchestration, tool use, and context management. Drive architecture, reliability, security, authorization, troubleshooting, and technical leadership while translating complex needs into practical user experiences.