Oracle Database Administrator

If you are a passionate database engineer who would love to sit at the intersection of traditional enterprise database engineering and the emerging Gen AI revolution — building next-generation, AI-powered data solutions that transform how tens of thousands of users interact with the largest Human Capital Management system then we have a perfect role for you and we would like to have a discussion with you. Education / Experience / Certification Bachelor's degree in computer science or related field (required) Master's degree in computer science, Data Science, or AI/ML (preferred) 10+ years of experience as a Database Developer/DBA in a fast-paced agile environment 7+ years as an Oracle Developer/DBA with expert-level PL/SQL and SQL skills 2+ years of hands-on experience with Generative AI, LLMs, or ML pipelines in a production environment (preferred) Deep expertise in database internals, expert-level PL/SQL, SQL, and Python/Shell programming AWS Certified Machine Learning Specialty (plus) Core Database Engineering Design and build scalable database services and solutions to complex business problems Debug critical database issues and provide root-cause analysis with long-term solutions Research, Design, Develop, and/or modify applications using SQL, PL/SQL, Python, and AI-assisted development tools Prototype solutions and recommend adoption of new technologies including Generative AI and LLM-powered database tooling Development and Deployment of application database changes/releases across production and non-production environments Build and maintain LLM-powered database assistants using frameworks such as LangChain, LlamaIndex, or similar Develop Retrieval-Augmented Generation (RAG) solutions using structured/unstructured data from Oracle and PostgreSQL databases Integrate AI-powered query optimization tools to enhance database performance tuning workflows Leverage GitHub Copilot, Amazon Q, or similar AI coding assistants to accelerate PL/SQL and Python development Build vector database integrations (pgvector, Oracle AI Vector Search) for semantic search capabilities Evaluate and adopt AI/ML model serving patterns (batch vs. real-time inference) for database-adjacent workloads Drive prompt engineering best practices for database-related AI applications #J-18808-Ljbffr