עדיין מחפשים עבודה במנועי חיפוש? הגיע הזמן להשתדרג!
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
מלאה
25,000-35,000 ₪
we are looking for a AI Engineer.
You will build the shared platform layer that makes agentic AI extensible, measurable, and self-improving. This is the foundation beneath everything: the orchestration engine, the skills registry, the evaluation harness, the guardrails system, the memory service, the integration gateway, and the tracing infrastructure - consumed by both the real-time conversational and offline video generation runtimes.
Your work is what separates it works for one customer with engineering involvement from it works for many customers across many domains without bespoke engineering per customer. You build the machinery once; customer-facing teams and eventually customers themselves extend it.
You will build the shared platform layer that makes agentic AI extensible, measurable, and self-improving. This is the foundation beneath everything: the orchestration engine, the skills registry, the evaluation harness, the guardrails system, the memory service, the integration gateway, and the tracing infrastructure - consumed by both the real-time conversational and offline video generation runtimes.
Your work is what separates it works for one customer with engineering involvement from it works for many customers across many domains without bespoke engineering per customer. You build the machinery once; customer-facing teams and eventually customers themselves extend it.
Requirements:
4+ years building production backend/platform systems - distributed services, APIs, async processing, and systems that serve multiple consumers. Python primary; experience with high-throughput, low-latency services.
Deep hands-on experience with LLMs in production - not just using them, but building the infrastructure around them: orchestration, tool calling, retrieval pipelines, context management, prompt chaining, and multi-agent coordination. Proficiency with LangChain, LangGraph, or equivalent orchestration frameworks.
Experience building platform primitives - registries, gateways, evaluation frameworks, tracing systems, or similar shared infrastructure that other teams build on top of. You understand contracts, versioning, and what it means to ship a platform rather than a feature.
Demonstrated ability to make build vs. adopt decisions - you have integrated open-source tooling into production systems, understood its boundaries, and built the proprietary layer where needed.
Experience with RAG systems at depth - indexing strategies, retrieval evaluation, chunking, re-ranking, hybrid search, and the failure modes (context pollution, retrieval misses, conflicting sources). Familiarity with vector databases (Pinecone, Weaviate, Qdrant, or similar) and embedding models.
Familiarity with real-time system constraints - you understand how latency budgets, deadlines, and streaming shape architecture differently than batch systems.
Strong systems design skills - you can design a registry, a gateway, a memory service, or an evaluation harness that serves multiple consumers with different needs (internal teams and external customers, real-time and offline paths).
4+ years building production backend/platform systems - distributed services, APIs, async processing, and systems that serve multiple consumers. Python primary; experience with high-throughput, low-latency services.
Deep hands-on experience with LLMs in production - not just using them, but building the infrastructure around them: orchestration, tool calling, retrieval pipelines, context management, prompt chaining, and multi-agent coordination. Proficiency with LangChain, LangGraph, or equivalent orchestration frameworks.
Experience building platform primitives - registries, gateways, evaluation frameworks, tracing systems, or similar shared infrastructure that other teams build on top of. You understand contracts, versioning, and what it means to ship a platform rather than a feature.
Demonstrated ability to make build vs. adopt decisions - you have integrated open-source tooling into production systems, understood its boundaries, and built the proprietary layer where needed.
Experience with RAG systems at depth - indexing strategies, retrieval evaluation, chunking, re-ranking, hybrid search, and the failure modes (context pollution, retrieval misses, conflicting sources). Familiarity with vector databases (Pinecone, Weaviate, Qdrant, or similar) and embedding models.
Familiarity with real-time system constraints - you understand how latency budgets, deadlines, and streaming shape architecture differently than batch systems.
Strong systems design skills - you can design a registry, a gateway, a memory service, or an evaluation harness that serves multiple consumers with different needs (internal teams and external customers, real-time and offline paths).
This position is open to all candidates.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
תל אביב - יפו
ערב
7 חודשים
