Вакансии: AI Engineering

Develop AI-powered tools, workflows, and infrastructure that improve the delivery, quality, and scale of security integrations and content for security operations platforms.

Lead the design and development of large-scale machine learning inference and data processing systems for production workloads. Collaborate with ML researchers and infrastructure teams to build scalable, efficient, and reliable solutions.

Lead the end-to-end design, development, optimization, and deployment of production-grade AI systems. Projects focus on either LLM-based agentic systems or computer vision, including real-time video analytics, depending on expertise and business needs.

Build, implement, and maintain AI-powered systems that automate knowledge collection, analysis, competitive intelligence, and internal knowledge sharing. Own projects from concept through delivery and adoption while partnering with information systems, IT, data science, and internal stakeholders.

Lead the design, development, and production delivery of generative AI capabilities for a consumer relationship guidance platform. Own AI infrastructure, LLM integrations, prompt experimentation, and cross-team enablement.

Join an architecture team designing storage platforms for AI training, inference, KV cache, retrieval-augmented generation, and related workloads. The role is based in Israel and involves close collaboration with hardware architects, software architects, customers, and partners.

Design and build the intelligence layer for an autonomous offensive-security platform. Develop AI agents that discover vulnerabilities, generate exploits, and adapt penetration-testing workflows in real time.

Build and scale a cloud-native machine learning platform and AI-agent infrastructure that helps data science teams deliver and operate production models.

Analyze data systems and business processes, identify AI opportunities, and lead the design and implementation of advanced data and AI solutions within a large organization.

Develop and optimize production-grade LLM inference software, GPU kernels, and distributed systems across data center and edge hardware platforms.