Senior ML & LLM Platform Engineer

About Us

JLL (NYSE: JLL) is a Fortune 200 company and a global leader in commercial real estate. We are a $20B+ business with over 100,000 employees worldwide and the stability and resources to invest seriously in AI. Under our Accelerate 2030 strategy, data and AI are the core pillars of how the company will operate and grow.

The Tel Aviv technology hub is at the center of that effort. We design, build, and operate production-grade AI systems that shape how decisions are made across JLL's diverse businesses.

The position

We are looking for a Senior ML & LLM Platform Engineer to join the Enterprise Data Science Group, an outstanding MLOps and LLMOps engineer who loves GenAI and running AI in production at scale, and thrives on:

• Serving open-weight LLMs at scale: GPU capacity and autoscaling, throughput, batching, quantization, caching, cost per token, use of tools, and more.
• Setting the MLOps and LLMOps standards for the group: experiment tracking, model and prompt registries, infrastructure-as-code, release practices, etc.
• Evaluation and observability for non-deterministic systems: monitoring, regression suites, and cost tracking for LLM pipelines.
• Shaping our development and pre-production environments into a best-in-class platform for testing and experimentation, where large-scale experiments are fast, reproducible, tracked, and safe to run against real data.
• Improving the CI/CD, engineering, and cloud foundations behind our ML/LLM pipelines: performance, cost, and reliability with data at scale.
• Designing agentic AI services and applications, including multi-agent systems and visual interfaces, taking them from prototype to production, while working with data scientists on ML and GenAI technologies (agents, RAGs, fine-tuning, hosting open weight models, MCPs) that raise the bar of accuracy and impact.
  
  

• 5+ years in MLOps / ML / AI / data / software engineering, with production systems you deployed and operated, and a proven track record of working alongside data scientists and researchers.
• Hands-on experience with GenAI technologies (LLMs, vector DBs and RAG, MCP, agent platforms, open-weight models) and with serving them at scale: orchestration, architecture, caching, monitoring, and ownership of latency, throughput, and cost.
• Fluency in the ML/LLM Ops toolchain: experiment tracking, model and prompt registries, production monitoring (MLflow or equivalents), etc.
• Deep understanding of LLM architectures: MoE (Mixture-of-Experts), attention variants, tokenization, quantization, KV caching, and batching.
• Familiarity (practical experience advantage) with distributed training and inference parallelism strategies - FSDP (Fully Sharded Data Parallel), data, tensor, and pipeline parallelism.
• A generalist mindset: comfortable venturing beyond ops into data engineering, agent development, and data science when the problem calls for it.
• A BSc (MSc an advantage) in computer science, mathematics, or another quantitative field, or equivalent experience.
  
  

• An entrepreneurial mindset: following emerging technologies closely, spotting what would keep us at the state of the art, and building support across the organization to make it happen.
• Strong data engineering foundations across cloud and DevOps: AWS/Azure/GCP, Databricks/Snowflake, Spark, infrastructure-as-code, CI/CD, and scheduled data pipelines.
• A track record in data science or applied research, or in building agentic systems.