AI Engineer – New Ventures
פורסם 10 במאי · 0 מועמדים
התפקיד במילים פשוטות
מהנדס/ת AI זה יצטרף/תצטרף לצוות New Ventures ב-monday.com כדי לפתח מוצרים חדשים מבוססי AI מאפס. התפקיד כולל עיצוב יסודות AI, ארכיטקטורות סוכנים ומערכות חכמות, וכן עבודה על תשתית ליבה, תזמור סוכנים, איכות מודלים ושיפור מתמיד בייצור.
- Proven experience building and deploying production-grade AI systems (beyond prototypes or demos)
- Strong software engineering foundation with 4+ years of experience in backend or full-stack development
- Deep familiarity with modern AI tooling and ecosystems (LLM APIs, embeddings, vector databases, RAG pipelines)
- Hands-on experience designing evaluation and monitoring systems for LLM-based applications in production
- Experience designing or working with agent-based systems (e.g., ReAct, tool use, multi-step reasoning loops)
- Experience building AI-native or agent-based products from scratch
- Familiarity with LLM observability tools, tracing, and debugging workflows
- Experience with real-time systems, WebSockets, or collaborative environments
- Background in rapid prototyping, experimentation, or startup-like environments
חולץ מתיאור המשרה · מתעדכן אוטומטית
למי זה מתאים
התפקיד מתאים למהנדסי/ות AI מנוסים/ות עם למעלה מ-4 שנות ניסיון בפיתוח backend או full-stack, וניסיון מוכח בבנייה ופריסה של מערכות AI ברמת ייצור. הוא אידיאלי למי שמכיר/ה כלי AI מודרניים, בעל/ת ניסיון בתכנון מערכות הערכה וניטור ליישומי LLM, ומבין/ה היטב עיצוב מערכות, מדרגיות ואמינות בסביבות מבוזרות.
תיאור המשרה המלא
המשרה המקורית · נשמר לעיוןNew Ventures at monday.com is expanding into new, AI-native product areas, and we’re looking for an exceptional AI Engineer to join our fast-moving, high-impact team. Our team develops new products from the ground up, combining startup velocity with the scale, reach, and engineering excellence of monday.com. The team built workcanvas.com and workassets.ai, and continues to explore and launch new product ventures. As part of a small, highly talented engineering group, you’ll play a key role in shaping the AI foundations, agent architectures, and intelligent systems powering these products and future initiatives. This is a rare opportunity to define how AI-native products are built at monday.com—from core infrastructure and agent orchestration to model quality, evaluation, and continuous improvement in production . #LI-DNI
Requirements: Proven experience building and deploying production-grade AI systems (beyond prototypes or demos) Strong software engineering foundation with 4+ years of experience in backend or full-stack development Deep familiarity with modern AI tooling and ecosystems (LLM APIs, embeddings, vector databases, RAG pipelines) Hands-on experience designing evaluation and monitoring systems for LLM-based applications in production Experience designing or working with agent-based systems (e.g., ReAct, tool use, multi-step reasoning loops) Strong understanding of system design, scalability, and reliability in distributed environments Experience running structured experiments (e.g., A/B tests, prompt/model comparisons) and using data to drive decisions Ability to navigate ambiguity and rapidly evolving technologies Strong communication skills and a collaborative, product-oriented mindset Advantages: Experience building AI-native or agent-based products from scratch Familiarity with LLM observability tools, tracing, and debugging workflows Experience with real-time systems, WebSockets, or collaborative environments Background in rapid prototyping, experimentation, or startup-like environments
שאלות על המשרה
- המשרה לא ציינה שכר. אנחנו מציגים שכר רק כשהמעסיק מפרסם אותו.
- Proven experience building and deploying production-grade AI systems (beyond prototypes or demos), Strong software engineering foundation with 4+ years of experience in backend or full-stack development, Deep familiarity with modern AI tooling and ecosystems (LLM APIs, embeddings, vector databases, RAG pipelines), Hands-on experience designing evaluation and monitoring systems for LLM-based applications in production, Experience designing or working with agent-based systems (e.g., ReAct, tool use, multi-step reasoning loops)