AI Data Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an AI Data Engineer based in the United States.

As an AI Data Engineer, you will build and optimize the data infrastructure that powers intelligent products and AI-enabled applications. You will design scalable data pipelines, develop automated data preparation workflows, and implement synthetic data generation solutions to support evolving AI workloads. Your responsibilities will include data modeling, storage architecture, retrieval systems, and automated validation to ensure data reliability and accessibility. Working closely with product and engineering teams, you will help transform complex data into high-quality resources that enable effective AI development and deployment. This opportunity combines modern data engineering practices with the challenges of building dependable infrastructure for next-generation AI applications. It is ideal for a technically driven professional who enjoys solving complex problems and developing scalable, high-performance data solutions in a remote, project-based environment.

• AI Data Pipeline Development: Design, build, and maintain scalable data pipelines that support AI workloads, intelligent products, and data-intensive applications.
• Synthetic Data Generation: Develop and maintain automated workflows for generating synthetic data to support AI development, testing, validation, and product improvement.
• Data Preparation & Processing: Build reliable processes for collecting, transforming, structuring, and preparing large-scale datasets for use in AI-enabled applications.
• Data Modeling & Architecture: Design data models and storage solutions that support efficient data organization, scalability, accessibility, and long-term maintainability.
• Data Validation & Quality Assurance: Implement automated validation processes, quality controls, and monitoring mechanisms to ensure data accuracy, consistency, completeness, and reliability.
• Storage & Retrieval Systems: Develop and optimize backend data systems and retrieval mechanisms that enable efficient access to relevant information for AI applications and intelligent products.
• Scalable Backend Engineering: Build and maintain robust data infrastructure capable of handling large datasets, evolving workloads, and changing application requirements.
• AI Product Integration: Collaborate with product managers, software engineers, and AI development teams to align data infrastructure with application requirements and product objectives.
• Workflow Automation: Identify opportunities to automate repetitive data engineering tasks, improve processing efficiency, and reduce manual intervention across data workflows.
• Performance Optimization & Troubleshooting: Investigate data pipeline failures, storage limitations, and retrieval bottlenecks, implementing improvements that strengthen system performance and reliability.
• Continuous Improvement: Evaluate data engineering practices and emerging approaches to improve scalability, maintainability, and the effectiveness of AI data infrastructure.

Requirements

• AI Data Engineering Experience: Practical experience designing, developing, or maintaining scalable data pipelines for AI workloads, intelligent products, or other data-intensive applications.
• Python Proficiency: Strong programming skills in Python for data processing, workflow automation, pipeline development, and backend data engineering.
• Data Generation & Preparation: Experience developing automated data generation, data transformation, or data preparation workflows to support complex application requirements.
• Data Modeling & Storage: Demonstrated ability to design data models and storage solutions for large-scale datasets, with an understanding of scalability, performance, and efficient data access.
• Data Validation & Quality Controls: Experience implementing automated validation, data quality checks, and monitoring processes to maintain reliable and consistent datasets.
• Backend Data Systems: Ability to develop dependable backend data infrastructure and retrieval mechanisms that support AI-enabled products and applications.
• Scalability & Performance: Understanding of scalable data architecture, efficient processing techniques, and approaches to optimizing data workflows and system performance.
• AI Application Integration: Experience collaborating with product or engineering teams to support the data requirements of AI-driven applications and intelligent products.
• Problem-Solving Skills: Strong analytical thinking and troubleshooting abilities, with a proactive approach to resolving technical challenges and improving data reliability.
• Communication & Collaboration: Ability to work effectively across multidisciplinary teams, translate product needs into technical solutions, and communicate complex engineering concepts clearly.
• Preferred Experience: Familiarity with Microsoft 365 Copilot or similar enterprise AI products is an advantage.
• Remote Work Readiness: Ability to work independently in a remote environment, manage priorities effectively, and contribute to contract-based project delivery.
• Employment Conditions: This is a contract opportunity based remotely in the United States. Applicants should be prepared to provide their current location, work authorization details, desired hourly rate, and current contract end date during the application process.

Benefits

• Competitive Hourly Rate: Indicative compensation ranging from $67 to $77 per hour, depending on relevant experience, technical expertise, and project requirements.
• Remote Work Flexibility: Work remotely from within the United States.
• Contract-Based Engagement: Gain hands-on experience contributing to AI and data engineering projects in a project-oriented environment.
• Exposure to AI Innovation: Work on data infrastructure and workflows that support intelligent products and next-generation AI applications.
• Modern Engineering Challenges: Develop expertise in scalable data pipelines, synthetic data generation, automated validation, data modeling, and retrieval systems.
• Technical Growth: Strengthen your data engineering and backend development skills while working on complex, large-scale data challenges.
• Cross-Functional Collaboration: Partner with product and engineering teams to deliver reliable data solutions aligned with real-world AI application needs.
• Impactful Work: Help build the foundational data systems that enable intelligent products to operate effectively and reliably.
• Employment Type: Contract.
• Experience Level: Entry-level listing, with practical experience in scalable data engineering and AI-related data workflows required.