Top Artificial Intelligence Jobs in Israel

Design, develop, and deploy scalable Generative AI solutions. Implement platform components, drive engineering best practices, collaborate with product and business teams, lead end-to-end AI system development from research to production, and follow AWS best practices.

Lead product vision and roadmap for SolarEdge's energy intelligence data platform, focusing on architecture, shared services, and cross-functional partnerships.

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.

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

The role involves partnering with various business teams to drive AI transformation, identify opportunities, and manage full lifecycle AI solutions to improve operations and save time.

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 role involves collaborating with teams to develop AI solutions, improve data quality, and enhance engineering productivity through automated tools and workflows.

Own the technical post-sale journey: onboard customers, analyze workflows, assess data readiness, configure AI models and integrations, deploy end-to-end solutions, define success metrics, drive adoption, and manage security and IT approvals.

Lead and scale the company's Data & AI organization: define strategy and roadmap, build and mentor Data Engineering leaders and teams, own architecture and platform reliability for data warehouses, lakes, streaming and analytics, drive DataOps/MLOps and governance, partner with cross-functional leaders to deliver measurable business outcomes, and enable future AI initiatives.

Drive technical vision and research for next-generation AI infrastructure and hardware-software co-design across GPU computing, RDMA, NVLink, storage, and large-scale distributed systems; lead PoCs, analyze system bottlenecks, and influence product and platform direction for massive-scale AI training and inference environments.

Join an SRE team to build and maintain scalable, reliable infrastructure. Develop automation, CI/CD and IaC, monitor performance and KPIs, run incident response and postmortems, and improve observability using logging, tracing, and metrics.

Lead definition and hands‑on creation of AI‑native customer experience solutions. Own AI capabilities across the CX platform, prototype and validate AI product experiences, drive AI‑first strategy, deliver roadmaps grounded in validated concepts, collaborate cross‑functionally, and ensure enterprise security, governance, and readiness.

The Senior Frontend Engineer will design and build complex UIs for AI products, focusing on component architecture, accessibility, and system reliability while collaborating with various teams and mentoring engineers.

Map manual business processes and identify automation opportunities; design, build, test, and ship AI-powered automations and agents; integrate systems via APIs/webhooks; apply LLMs, RAG, and agent memory; maintain automations and train process owners; collaborate with Finance, HR, Sales, Support, and IT.

As an AI Forward Deployed Engineer, you will embed with strategic accounts to build AI solutions that address procurement challenges, while managing customer relationships and delivering outcomes.

The AI & IT Operations Manager will implement AI tools to enhance operations, manage IT infrastructure, ensure security compliance, and support internal stakeholders.

As a Senior HPC and AI Cluster Administrator, you'll deploy and manage HPC/AI clusters, maintain CI/CD pipelines, and troubleshoot across software and hardware.

The Senior AI ML Solution Engineer designs AI development pipelines, evaluates ML methods for code generation, and ensures quality in AI-generated code. Responsibilities include creating evaluation systems, leading proofs of concept, and collaborating on risk-based development strategies.

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.

As an AI Engineer, you will design and develop LLM-based intelligent agents, integrate tools, collaborate with teams, and improve AI models.

The Software Engineer will develop and maintain observability products, solve complex problems in cloud environments, and collaborate on AI integration.

Lead the AI engineering maturity program for a 100-person R&D/QA/DevOps org: drive hands-on agentic tooling adoption, build safe autonomous agent infrastructure, create spec-driven workflows and self-verifying test loops, own roadmap and observability, and enable engineers across Java/Python/C#/.NET/C++/Oracle stacks to use AI-assisted development at scale.