AI Engineer

One of Israel’s fastest-growing Cloud Infrastructure and Kubernetes Optimization technology companies. The company develops an autonomous, AI-driven automation platform engineered to dynamically manage, scale, and optimize compute resources across production Kubernetes environments in real time. The platform seamlessly integrates into enterprise multi-cloud architectures to maximize system performance while drastically slashing cloud infrastructure costs by up to 80%. Backed by hundreds of millions of dollars in top-tier venture capital funding, the enterprise employs over 100 professionals globally, with its primary core R&D organization located in Israel. Located in central Tel Aviv (Azrieli Sarona area, walking distance from the train station), operating on a full-time, on-site work model. Role Description- Joining a specialized, high-impact AI Engineering team (reporting to the AI Director) to build and scale production-grade LLM-based architectures, Autonomous Agents, and Agentic workflows. Developing backend services and scalable AI infrastructure primarily using Python, bridging cutting-edge GenAI research with core production systems. Architecting advanced Retrieval-Augmented Generation (RAG) pipelines, integrating Vector Databases, and building resilient Multi-Agent systems. Utilizing modern AI frameworks and protocols including LangGraph, LangChain, MCP (Model Context Protocol), and Vector DBs. Partnering closely with Core Backend, Kubernetes, and Cloud Infrastructure teams to deliver mission-critical AI platform capabilities. Requirements- 3+ years of hands-on software engineering experience focused on Backend Development or AI/ML Engineering – Mandatory Extensive, production-grade programming experience with Python – Mandatory Hands-on experience developing with Large Language Models (LLMs) and Generative AI technologies – Mandatory Proven track record of building and deploying AI / Machine Learning systems to Production – Mandatory Practical experience building AI Agents, Agentic Systems, RAG pipelines, or using frameworks like LangChain / LangGraph – Mandatory 153968 Show more Show less