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Compunnel, Inc. seeks an accomplished database engineer at the intersection of enterprise database engineering and Gen AI to build AI-powered data solutions for a massive Human Capital Management system.
Join a fast-paced team to design scalable services, optimize PL/SQL/SQL, develop ML pipelines, and implement RAG/LLM tooling with LangChain and LlamaIndex. You will deploy changes across environments and contribute to prompt engineering for AI applications.
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.
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)
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