Senior Software Engineer

AI Engineer

JLL | AI Engineering | Full-Time

Tel Aviv, Israel

About The Role

JLL's central AI Engineering team builds the AI systems that power a $20B global real estate firm — from LLM gateways and RAG infrastructure to agent-driven workflows used daily by thousands of employees.

You will join the Solution Engineering team in Tel Aviv, working forward-deployed alongside business stakeholders and the broader AI platform team. Your job is to translate platform capabilities into working AI applications: integrations, agents, and automation that close the gap between what the AI platform can do and what the business actually needs.

This is a hands-on engineering role. You will write production code every day, work closely with a senior tech lead in Tel Aviv, and operate with AI tools as a primary lever — not a secondary convenience.

What You'll Do

• Build AI-powered features and integrations on top of JLL's core AI platform: LLM gateway, shared RAG services, and MCP toolchain.
• Design and implement AI agents for business workflows — using Claude, MCP, and the Claude Agent SDK.
• Work directly with stakeholders to turn fuzzy requirements into shipped software.
• Own your work end-to-end: design, code, test, deploy, and monitor.
• Contribute to platform patterns and internal tooling that other engineers reuse.
• Keep up with the AI ecosystem and bring relevant signal back to the team.
  
  

• Shipped production software in Python and at least one of TypeScript or Node.
• Built and deployed LLM-powered features: prompt engineering, RAG pipelines, or agentic workflows.
• Comfortable with REST APIs and event-driven patterns.
• Worked with cloud-native infrastructure (AKS, Azure, or AWS) and modern CI/CD.
• Used AI coding tools (Cursor, Claude Code, or equivalent) as a core part of your engineering workflow — not occasionally.
  
  

• You write clean, maintainable code and own it through to production.
• Strong written communication in English — cross-team, cross-timezone work demands clarity.
• Curious and pragmatic: you follow the AI ecosystem closely but ship practical solutions.
• Comfortable moving between ambiguous requirements and concrete deliverables.
• You raise problems early and prefer solving them over reporting them.
  
  

• Hands-on experience with agent frameworks (Claude Agent SDK, LangGraph, or similar).
• Familiarity with vector stores and hybrid retrieval (Qdrant, Pinecone, or similar).
• Experience with MCP tooling or LLM observability.