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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Oracle Database Engineer - **Company:** Iflowsoft Solutions Inc - **Location:** Parsippany-Troy Hills, NJ, United States - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Amazon Web Services, Unit Testing, Microsoft Azure, Cloud Computing, Software Quality, Databases, Continuous Integration, Information Engineering, Data Systems, Database Development, Software Debugging, Software Design Patterns, Linux, DevOps, JSON, Python (Programming Language), PostgreSQL, Unix Shell, Machine Learning, Microsoft SQL Server, Oracle Databases, Oracle (Applications), Oracle SQL Developer, Performance Tuning, Query Optimization, Red Hat Enterprise Linux, Ansible, Standard Sql, Azure Machine Learning, Search Technologies, Shell Script, PL-SQL, SQL Databases, Unstructured Data, GitHub Copilot, Large Language Models, Database Optimization, Prompt Engineering, Generative AI, Git, Build Management, Information Technology, Bitbucket, Machine Learning Operations, Api Design, Jenkins, Artifactory - **Published:** August 22, 2026 - **Apply:** https://www.dice.com/job-detail/285848f7-76e8-48a5-b368-afaa70056186 ## About the Role · 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 · Oracle Certified Professional (OCP) (huge plus) · AWS Certified Machine Learning Specialty (plus) Technical Skills Required · Databases: Oracle, MSSQL, PL/SQL, SQL · Programming: Python, Shell scripting · DevOps: Git, Jenkins, Ansible, Artifactory · Cloud: AWS · OS: Linux/RedHat · Oracle Features: VPD, Partitioning, Expert-level Performance Tuning, JSON data in databases · AI/ML: Prompt Engineering, NL2SQL, Text-to-SQL systems Preferred · Databases: RDS Oracle, RDS PostgreSQL · AI/ML: LangChain, LlamaIndex, RAG pipelines, Vector stores(pgVector) · LLM Platforms: OpenAI API, Azure OpenAI, AWS Bedrock · DevOps: Bitbucket, CI/CD for AI model deployment · Cloud AI Services: AWS SageMaker, Azure ML · Oracle Advanced Features: Golden Gate, Cloud Control, Golden Gate BDA ## Description 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., · 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 Quality & Leadership · Self-reliant, hands-on technical leader driving code quality, AI governance, and database AI practices · Collaborate with database peers, product owners, UX/UI, and globally distributed teams on AI-enhanced database solutions · Define engineering best practices including AI-assisted Test-Driven Development (TDD) and automated unit testing for database development · Mentor teams on AI tool adoption, prompt engineering, and AI use in data engineering · Develop specifications for new database services incorporating AI-native design patterns · Meet deadlines and manage multiple, dynamic priorities in an agile environment ## Related Videos - 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