LLM Engineer (AI)

An established Israeli Fintech company is developing a pioneering AI and Big Data platform that maps the "financial internet" to combat money laundering and global tax evasion.

By extracting and cross-referencing vast amounts of unstructured data from the web including deep analysis of cryptocurrency networks the company identifies fraud and deanonymizes digital assets for government and law enforcement agencies worldwide.

Following a significant growth stage backed by top-tier global VCs, the company is expanding its R&D center to meet increasing global demand.

The company is located in Central Israel (prime location near major transport hubs) and operates a hybrid work model with one day remote flexibility, emphasizing a strong work-life balance and excellent benefits.

Role Overview:

• Leadership & Growth: Establishing and leading a newly formed AI team within the R&D department, reporting directly to the VP R&D. You will manage an initial team of 1-4 engineers with plans for further expansion.
• Production AI Excellence: Taking end-to-end responsibility for the development and deployment of advanced AI systems in a high-scale production environment.
• Advanced NLP & Extraction: Architecting and optimizing pipelines for Information Extraction, Reasoning, and Classification from unstructured sources such as social media, documents, and business listings.
• LLM Orchestration: Designing sophisticated systems using RAG, Autonomous Agents, and frameworks like LangChain or LlamaIndex.
• Data Integration: Leveraging VectorDBs (FAISS/Pinecone), Knowledge Graphs, and external APIs to build a comprehensive financial intelligence engine.

Requirements:

• 2+ years of experience in a technical leadership or management role – Mandatory.
• 5+ years of professional experience in Machine Learning – Mandatory.
• 2+ years of hands-on experience with LLM development – Mandatory.
• Proven track record in developing and deploying RAG architectures – Mandatory.
• Strong proficiency in Python and modern AI/ML frameworks.
• Deep understanding of AI system architecture, evaluation loops, and production-grade deployment.