לוח משרות דרושים הייטק AI עבודה זמנית, לחיפוש זה נמצאו 2 משרות !

We're looking for an AI and Knowledge Manager to own the intersection of AI-driven support and our Knowledge hub (aka. Help center). You'll make sure our AI tools and self-service resources work as one system, so customers get fast, accurate, and consistent answers no matter which channel they land on.
You'll shape the AI chatbot strategy end to end, from prompt design to failure detection, while owning the Knowledge hub (aka. Help center) roadmap, content, and voice. You'll partner closely with Support, CS, and R&D to keep our knowledge base sharp, train agents on AI tools, and turn deflection data into concrete improvements.
Hands-on experience designing and optimizing LLM prompts, and using AI tools to scale documentation and support workflows, is a must for this role.
This role is based in Tel Aviv. We work in a hybrid model, with 3 days a week in the office. This is a Monday to Friday role.

We are looking for an Applied AI Engineer to join the MECI Interfaces team within the Software Engineering organization. The MECI Group enables our algorithmic development flow by building scalable CI, build, test, and AI-driven tooling that supports the full development lifecycle. Our platforms allow us to scale efficiently and increase development velocity, while maintaining high standards of reliability, collaboration, and developer well-being.
As an Applied AI Engineer, you will be at the forefront of integrating Large Language Models (LLMs) and agentic workflows into our internal platforms. Your primary goal will be to solve complex problems across the Software Development Life Cycle (SDLC) by building intuitive, AI-powered tools that directly support our developers and internal users. This role offers the unique opportunity to both expand the intelligence of our existing developer tools and architect entirely new workflows from the ground up in a highly technical, fast-paced environment.
What will your job look like?
Design, develop, and deploy AI-driven internal tools and agentic workflows that accelerate the SDLC and reduce friction for our developers.
Expand the capabilities of existing platforms by embedding smart, context-aware LLM features.
Identify high-impact bottlenecks across the engineering organization and build zero-to-one AI solutions to solve them.
Architect robust orchestration layers around LLMs, focusing on practical implementations of custom system "skills," tool calling, Model Context Protocol (MCP) integrations, memory management, and Retrieval-Augmented Generation (RAG).
Maintain a framework-agnostic approach, rapidly evaluating and adopting the most effective AI models, APIs, and open-source techniques as the landscape evolves.