23902 - Data Scientist - GenAI

We are looking for a hands-on Data Scientist to join our AI delivery team and build end-to-end AI solutions for enterprise clients.

The role focuses mainly on Generative AI solutions, such as RAG pipelines and LLM-based systems, including locally hosted / on-premise LLM deployments for privacy-sensitive environments.

It also includes additional work in classical Machine Learning and Data Science.

The position includes designing, deploying, and integrating AI solutions in highly secured local and air-gapped environments, working with embeddings and latent space representations to build effective retrieval and LLM-based systems, and participating in technical discussions and pre-sales activities.

This full-time position is in Lod and requires security clearance.

Responsibilities:

• Deploy scalable solutions using Docker, cloud platforms (AWS/GCP/Azure), and Win/Linux environments, in collaboration with DevOps teams.
• Design and deploy end-to-end ML/DL/GenAI solutions, including data analysis, feature engineering, model development, and performance monitoring.
• Apply AI algorithms and conduct statistical analysis on structured and unstructured data.
• Develop Generative AI solutions, including RAG pipelines and LLM-based applications.
• Deploy and serve open-source LLMs locally (e.g., Ollama, vLLM), including model selection, quantization, and hardware/performance trade-offs.
• Build and optimize embedding-based retrieval in latent space: select and evaluate embedding models, design chunking strategies, and apply hybrid search and reranking to improve retrieval quality.
• Integrate LLM APIs (cloud and local) and vector databases, and optimize GenAI system performance.

Requirements:

• 2+ years of experience in Machine Learning / Data Science, with hands-on experience delivering end-to-end GenAi solutions in production environments.
• B.A. or M.A. in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• Proficiency in Python and common ML\DL\GenAi libraries.
• Experience with RAG systems and LLM-based solutions.
• Solid understanding of embeddings and vector representations (latent spaces), including semantic similarity and vector search.
• Hands-on experience running open-source LLMs locally (Ollama or similar).
• Experience with Docker and cloud environments.
• Experience working in client-facing environments.
• Ability to pass a security clearance process (required).
• Work on-site at the client's offices.
• M.A. or Ph.D. in a relevant field.
• Experience with Kubernetes, CI/CD, or MLOps.•Experience monitoring GenAI solutions in production.
• Experience with vector databases (e.g., Qdrant, Chroma, pgvector) and RAG evaluation frameworks.
• Experience with on-premises or air-gapped AI deployments.
• Experience presenting technical solutions to clients (English and Hebrew).