Top Artificial Intelligence Jobs in Israel

Define and drive AIOps and AI-data-readiness strategy and roadmap; lead customer discovery with enterprise accounts; oversee deployment models for cost, governance, privacy, and compliance; take products from PRD to GA with R&D/QA/Support; measure adoption and business outcomes; act as AIOps subject-matter expert.

Evaluate and annotate LLM-generated conversations and text, identify quality trends and failure modes, perform high-quality labeling, analyze results, and partner with AI engineers and product managers to improve model performance and user experience.

Build and scale autonomous AI agents and their control plane for security tasks. Implement HITL feedback, stateful memory, evaluation frameworks, and integrate agents with big-data pipelines to enable goal-driven, tenant-safe security automation.

Lead and scale AI red-teaming and security research teams to design and execute adversarial attacks against LLMs, generative and agentic AI, build automated red-teaming engines, evaluate adversarial behaviors, and partner with product and engineering to implement guardrails, monitoring, and secure deployment practices.

Build and extend a production LLM agent platform: design agents and tools, implement integrations, own RAG ingestion and retrieval, define integration contracts, drive evaluation-led development, operate deployments and observability, and establish AI-specific engineering practices across frontend and backend.

Lead the company's internal AI strategy, drive AI adoption across departments, build agent platforms and machine-readable knowledge repositories, optimize processes with AI workflows, establish AI governance, security, and FinOps, and enable business units to use AI-assisted development.

Lead and manage multiple AI engineering teams to design, build, and scale AI-powered SaaS products. Set technical direction, remain hands-on, drive full engineering lifecycle, partner with Product, mentor team leads, and balance long-term architecture with fast delivery.

Build and own agentic workflows and AI platform capabilities that accelerate the R&D software development lifecycle. Discover bottlenecks, design and ship production-grade LLM/agent systems, create evaluations and safety/feedback loops, integrate platform infrastructure, and prove KPI-driven impact while partnering with engineering teams.

Lead and scale Upstream's global AI & API Security business: own revenue, pipeline, forecast and pricing; build the sales motion and playbook; sell strategic enterprise deals end-to-end; enable regional teams with training and deal support; run discovery, architecture, demos and POVs; represent the offering externally.

Serve as a technical trusted advisor embedding with customers to design, build, and deploy AI-powered workflows and MCP-based agents. Lead technical sessions, integrate with CRMs/ERPs, create reusable integration assets, translate AI concepts to business value, and drive post-go-live improvements while collaborating with Customer Success, Product, Support, and Engineering.

Perform offensive security research against agentic AI and LLM ecosystems, discover novel attack vectors, build tooling and test frameworks, analyze surrounding components (plugins, connectors, orchestration), and collaborate with product and engineering to turn findings into defenses.

Own and scale end-to-end user acquisition across paid channels using AI-driven research and automation. Develop and optimize campaigns, test growth opportunities, collaborate with product to improve conversion, and use data for fast performance decisions.

Lead end-to-end development and architecture of Via's customer-facing Voice AI agent. Build scalable backend services powering real-time conversational interactions, integrate and deploy LLMs and AI providers, drive product initiatives, and mentor engineers to elevate technical craft and AI capabilities.

Design, build, and maintain end-to-end AI solutions that unify engineering data for analytics, agents, copilots, and workflow automation. Consolidate infrastructure data, build pipelines, APIs, monitoring, and tooling, partner with engineering teams to define use cases, deploy scalable production systems, and drive continuous improvement and adoption.

As a Senior Full-Stack Engineer, you will develop features from ideation to production, make tradeoff decisions, collaborate with teams, and deliver technical solutions that impact business outcomes.

The AI Operations Engineer will automate workflows, build AI agents, and integrate tech solutions to improve operational efficiency in healthcare at Droxi.

Build and own end-to-end features for the Voyantis UA Platform, including backend services, APIs, databases, LLM agents, data pipelines, UI, monitoring and dashboards. Ensure performance, correctness, reliability and scalability while contributing to architecture, experimentation and operational excellence.

Design, implement, and maintain Go-based scheduler components for AI workloads on Kubernetes. Improve scalability for massive clusters, solve placement and optimization problems, lead code/design reviews, mentor engineers, and collaborate with upstream projects, contributors, and customers to translate production feedback into engineering improvements.

Lead development and adoption of AI-driven architecture workflows for NIC and switch platforms. Define roadmap, build agentic flows with AI engineering, validate architecture outputs, collaborate with architects and leadership, run betas, and drive organization-wide tooling adoption to shorten design cycles and raise output quality.

Lead technical direction and roadmap for an open-source Kubernetes AI scheduler. Design scalable scheduling architectures for massive GPU clusters, solve optimization and distributed-systems challenges, engage with community and upstream projects, and mentor engineers.

Develop and implement novel machine learning algorithms and foundation-model components for biomedical/clinical outcome prediction. Lead research, experimentation, distributed training, benchmarking, and production-quality implementation while collaborating with cross-functional teams and contributing to publications and IP.

The Software Engineer will benchmark AI platform performance, develop automation tools, optimize workflows, and document findings in a collaborative, open-source environment.

Lead a hands-on engineering squad building real-time, decision-making AI systems at scale. Own architecture, ship critical system components, mentor engineers, embed AI into team workflows, and define the engineering culture and processes.

Lead end-to-end AI product initiatives to design, build, and ship agentic workflows that automate property management processes. Define strategy, OKRs, metrics, run experiments, and collaborate cross-functionally to deliver production AI features with strong UX and human oversight.

Lead end-to-end feature development for Daisy's AI-driven building OS. Design and ship agentic workflows with human oversight, collaborate cross-functionally, and push coding agents to increase developer velocity and democratize development for non-coders.