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
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