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.

LLM Engineer (AI)

Gotfriends

תל אביב - יפו

LLM Engineer (AI)

Gotfriends

תל אביב - יפו
היברידית
25,000-35,000 ₪ (הערכה מבוססת AI)
זוהי הערכת טווח שכר מבוססת AI ולא פרסום של המעסיק

A Cyber & AI startup that has developed a platform enabling security teams to build and operate hundreds of micro-agents for focused security tasks, such as identity and access management, alert investigation, vulnerability remediation, endpoint management, and more.

The platform is already actively used by large, global enterprises and leading organizations across multiple industries.

Based in Tel Aviv, near the train line, operates a hybrid work model (two days per week from home), employs approximately 100 people, and has raised $100M in funding.

The role-

Be part of an LLM-focused development team, building AI capabilities from research to production.
Work extensively with RAG (Retrieval-Augmented Generation) techniques, including chunking strategies and retrieval optimization.
Take responsibility for AI development within the product, covering both model-level logic and production-grade infrastructure.
Develop and support AI-driven features such as text generation, code completion, and prompt engineering.
Build scalable, reliable AI systems deployed in production environments.

Requirements-

4+ years of experience in software development, with strong proficiency in developing LLM-based applications.
Expertise in developing cloud-based applications and distributed systems.
Strong analytical and problem-solving skills to navigate the complexities of AI and LLM integration.
Familiarity with a variety of AI models, including but not limited to large language models, and an understanding of their capabilities in tasks such as text generation, code completion, and more - An advantage.
Experience with prompt engineering techniques to achieve desired outputs from LLMs - An advantage