AI Developer

CyberArk is seeking a highly skilled AI Developer to design and implement AI-driven solutions as part of our internal Transforming How We Work program. In this role, you will build applications and pipelines that leverage large language models (LLMs) and enterprise data to drive intelligent automation and insights across the company. You will collaborate closely with our AI Machine Learning Engineer and Platform Architect to ensure solutions are scalable, well-architected, and seamlessly integrated into our internal developer platform. The ideal candidate will have a passion for using AI models, grounding them with relevant knowledge, and developing retrieval-augmented generation (RAG) pipelines with robust indexing for optimal performance. About the Team:  The team provides support to the entire Transforming Our Work initiative by building, automating and maintaining internal infrastructure in a manner that enables “self-service”, low/no code and enterprise solutions. It consists of development in support of internal tools, infrastructure for testing and development and enterprise technology support for CI/CD, DevOps and orchestration.    We are currently looking for an AI Developer to join us in our work to create AI solutions on our internal platform. Responsibilities: Design and Develop AI Solutions: Build and implement AI-powered applications and workflows on the internal AI platform that solve business problems and improve efficiency. Fine-Tune LLMs: Customize and fine-tune large language models (e.g., GPT-style models) to address specific use cases and improve their performance for CyberArk’s needs. Knowledge Grounding & Retrieval: Develop and optimize retrieval-augmented generation (RAG) pipelines, incorporating efficient retrieval systems to ground LLM outputs in accurate, up-to-date internal data. Create and maintain indexes (e.g., embedding vectors, knowledge bases) that enable fast and relevant information retrieval for AI solutions. Collaborative Solution Building: Work closely with data scientists and the AI Machine Learning Engineer to integrate machine learning models into end-to-end solutions. Coordinate with the AI Platform Architect to ensure your solutions align with architectural standards for security, scalability, and governance, and with the AI Infrastructure Engineer to deploy, monitor, and maintain these AI solutions in production environments. Testing and Iteration: Rigorously test AI models and applications, validate their accuracy and effectiveness (including handling of edge cases), and iterate based on feedback and performance metrics. Monitor Performance: Monitor and evaluate the performance of deployed AI models and solutions, identifying areas for improvement and implementing necessary adjustments for accuracy, efficiency, and reliability. #LI-EB1