AI Data Engineer

Accountabilities:: Design, develop, test, and maintain data pipelines and ETL processes for ingesting, transforming, and loading data from diverse sources. Build and optimize data architectures and models that support analytics, reporting, and operational requirements. Develop AI-enabled solutions using concepts such as prompt engineering, machine learning, agentic AI, and retrieval-augmented generation (RAG). Implement and manage CI/CD pipelines for data engineering workflows using Git and related development tools. Develop and deploy cloud-based solutions using Azure Functions and containerization technologies such as Docker and Kubernetes. Collaborate with DBAs and application developers to design and integrate data models, stored procedures, and APIs. Develop and support RESTful and SOAP APIs and integrate data and AI/ML components into broader applications. Ensure data quality and integrity through validation, monitoring, logging, and proactive issue resolution. Troubleshoot and resolve production issues while maintaining high availability and reliability of data solutions. Manage multiple tasks and competing priorities while delivering projects within an Agile development environment. Document data flows, processes, architectures, and technical solutions to support knowledge sharing and compliance. Present project status, technical work products, and complex concepts to technical and non-technical stakeholders. Stay current with emerging technologies and best practices across data engineering, AI, cloud, and DevOps. Requirements: Bachelor’s degree required. 5+ years of experience in data engineering, software development, or a related technical discipline. Must be able to obtain and maintain a Public Trust. Hands-on experience with AI methods and tools, including prompt engineering, machine learning, agentic AI, RAG, and tools such as Copilot and Codex. Strong programming experience with Java, Python, SQL, and PySpark. Experience designing and developing scalable data pipelines and ETL processes. Extensive experience with relational and NoSQL databases, including MySQL, PostgreSQL, SQL Server, and MongoDB, as well as stored procedure development. Experience working with Databricks. Experience developing and managing CI/CD pipelines using tools such as GitHub. Familiarity with Git, Azure DevOps, and modern version control and collaboration practices. Experience working in Agile development environments. Experience with API development, including RESTful and SOAP services. Familiarity with containerization and orchestration technologies such as Docker and Kubernetes. Strong analytical, troubleshooting, and problem-solving skills. Excellent verbal and written communication skills, with the ability to explain complex technical concepts to diverse audiences. Ability to work independently and manage multiple priorities in a fast-paced environment. Familiarity with security best practices and compliance requirements in federal environments is preferred. Experience with microservices, Spring Boot, and AI/ML component integration is advantageous. Experience supporting federal programs or large-scale data modernization initiatives is a plus. Previous consulting experience is preferred. Benefits: Contract position. Remote work arrangement. Atlanta, Georgia is preferred, with San Antonio, Texas also listed as a preferred location. Opportunity to work across data engineering, AI, machine learning, cloud, DevOps, and analytics. Exposure to modern technologies including Databricks, Azure Functions, Docker, Kubernetes, PySpark, GitHub, and AI development tools. Opportunity to contribute to federal programs and large-scale data modernization initiatives. Cross-functional collaboration with DBAs, application developers, and other technical stakeholders. Specific salary, healthcare, retirement, paid time off, and additional benefits were not specified in the source job description.