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Data Engineer, AI
AnvetaUnited StatesData Engineer, Ai PlatformRandstad is seeking a skilled Data Engineer, Ai Platform to join a premier client in the transportation and logistics sector in Washington, D.C. In this role, you will bridge
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CiscoUnited StatesThe application window is expected to close on: 06/30/2026Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.Meet the TeamAre you ready
Über
Randstad is seeking a skilled Data Engineer, Ai Platform to join a premier client in the transportation and logistics sector in Washington, D.C. In this role, you will bridge the gap between core data engineering and advanced AI orchestration, building the scalable pipelines and feature stores that power enterprise-level innovation. As a key member of the Digital Technology team, you will independently design and maintain production-ready systems that make data discoverable and reliable, ensuring that AI-driven insights are seamlessly integrated into the organization's mission-critical operations. Key Responsibilities
Pipeline & Framework Development: Design, build, and optimize automated data pipelines and AI integration workflows to support large-scale analytics and machine learning applications. Data Orchestration & Quality: Implement robust ETL/ELT processes, data transformations, and validation controls to ensure high-quality, compliant data assets. AI/ML Operationalization: Collaborate with data scientists and architects to develop feature pipelines, model serving patterns, and inference workflows within a cloud or hybrid environment. Architecture & Optimization: Performance-tune AI workloads, design reusable data views, and manage data contracts, row-level security, and caching strategies. Agile Collaboration: Participate in sprint planning and retrospectives, delivering high-quality technical documentation and lineage for all developed data products. Qualifications
Education: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field. Professional Experience: 2–4 years of hands-on experience in data engineering, software development, or data integration. Technical Proficiency: Strong mastery of Python and SQL, with a solid understanding of data/AI architecture (feature stores, MLOps, and model deployment). Platform Knowledge: Experience working with cloud data platforms (e.g., AWS, Azure, or GCP) and modern data lakehouse architectures. Problem Solving: Demonstrated ability to debug complex data systems and work independently within an Agile framework. Preferred Skills: Familiarity with metadata management, data observability, and automated CI/CD frameworks for AI/ML.
Sprachkenntnisse
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