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Senior AI Engineer at ZyG
Tel-Aviv, Tel-Aviv District, Israel -
Full Time

Start Date

Immediate

Expiry Date

18 Jul, 26

Salary

0.0

Posted On

19 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

LLM Integration, Agentic Workflows, RAG Systems, Prompt Engineering, Multi-agent Architectures, Vector Search, Python, LangGraph, CrewAI, ADK, Machine Learning, API Integration, System Evaluation, Data Management, Software Architecture

Industry

technology;Information and Internet

Description
The Role We are looking for a Senior AI Engineer to design and build the intelligence layer of ZyG. You will own the agentic workflows, LLM integrations, and reasoning pipelines that allow our AI agents to autonomously analyze markets, make decisions, and drive eCom growth at scale. What you'll do Design & Build Agentic Workflows: Architect multi-agent pipelines — including planning, memory, tool use, and decision loops — that power autonomous media buying and growth operations. Own LLM Integration: Select, prompt-engineer, fine-tune, and evaluate LLMs to produce reliable, high-quality outputs across diverse business tasks. Build RAG Systems: Develop retrieval-augmented generation pipelines with vector search and context management to ground agent reasoning in real business data. Drive Evaluation & Reliability: Define evals, build testing frameworks, and continuously improve agent output quality, consistency, and safety in production. Collaborate Across the Stack: Work closely with backend engineers to integrate AI capabilities into core product APIs, ensuring low-latency, production-grade deployment. Requirements 7+ years of engineering experience, with at least 2 years focused on LLM-based systems, agents, or applied ML in production. Agentic Systems Expertise: Hands-on experience building multi-agent architectures, tool-calling workflows, and orchestration frameworks (e.g. LangGraph, CrewAI, ADK, or custom). Prompt Engineering & Evals: You treat prompts as code — versioned, tested, and measured. You know how to systematically debug and improve LLM behavior. AI-Native Development: You actively use agentic coding tools (Claude Code, Cursor, etc.) to accelerate your own workflow. Advantages null
Responsibilities
Design and build agentic workflows, including planning, memory, and decision loops for autonomous AI agents. Develop RAG systems and integrate LLMs to ensure reliable, high-quality outputs for business tasks.