Mid-Level AI Data

2 weeks ago

Tel Aviv, Tel-Aviv District, Israel easybizy Full-time

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About the Position We are looking for an enthusiastic Mid-Level AI Data & Orchestration Engineer to design and scale the intelligent data layers and multi-agent systems powering our platform. This is a heavy backend and data-centric role. The core of our AI logic, orchestration, and graph-based memory systems is built using Python. You will be responsible for building complex, stateful multi-agent workflows, managing advanced Retrieval-Augmented Generation (RAG) pipelines, and engineering high-throughput ingestion pipelines. Alongside Python, you will utilize Node.js to integrate these AI services into our broader application backend and handle asynchronous workflows.

Key Responsibilities
�
� Agentic Graph Systems: Architect, deploy, and optimize complex, cyclic multi-agent workflows and stateful decision loops using Python (LangGraph, CrewAI, or AutoGen). �
� Advanced RAG & Data Engineering: Design and build semantic data pipelines. Implement advanced chunking strategies, hybrid search (keyword + vector), and metadata filtering to maximize retrieval precision. �
� Vector DB Management: Build automated data ingestion pipelines in Python to clean, parse, embed, and index large scale unstructured data into Vector Databases. �
� Backend Co-existence (Node.js): Write clean, async services in Node.js to bridge our core Python AI orchestration engines with our application layers, managing webhooks and API routing. �
� AI Observability & Evaluation: Implement logging, tracking, and evaluation frameworks (e.g., LangSmith, Phoenix, or LangFuse) to debug agent trajectories, track token budgets, and minimize latency. �
� Light Client Integration: Coordinate with front-end components occasionally to ensure token-streaming performance and handle WebSocket states gracefully. Requirements (What You Bring) �
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Experience:
3 to 5 years of professional software engineering experience, with a heavy emphasis on backend and data pipelines. �
� Python Mastery: Strong production experience writing clean, scalable, object-oriented Python (FastAPI, Pydantic) for data processing or machine learning operations. �
� AI Framework Fluency: Hands-on experience building production workflows using LangChain and LangGraph (or equivalent Python state-machine/agent frameworks). �
� Data Layer Expertise: Deep familiarity with Vector Databases (e.g., Pinecone, Qdrant, Milvus, Chroma) and traditional relational/NoSQL databases. �
� Node.js Literacy: Solid experience writing JavaScript/Node.js for microservices, API endpoints, or async event loops. �
� Core AI Concepts: Practical understanding of vector embeddings, distance metrics, reranking models, and context-window optimization. Advantages (Nice to Have) �
� Experience with data orchestration tools like Apache Airflow, Prefect, or Dagster. �
� Familiarity with creating custom servers using the Model Context Protocol (MCP). �
� Basic experience with Vue.js/Nuxt.js or React for building quick internal administrative playgrounds/testing tools. �
� Model Context Protocol (MCP): Hands-on experience building, extending, or integrating custom servers and clients using MCP to connect LLMs to data sources and tools.

Mid-Level AI Data

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Mid-Level AI Data

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