AI Data Engineer

Agility Partners is seeking a qualified Senior Backend Engineer - AI & Data Systems to fill an open position with a Fortune 15 company based in the greater Seattle area. This role will focus on building and improving synthetic data-generation systems, scalable data pipelines, cloud-based backend services, and automated data-quality frameworks that support next-generation AI experiences. The ideal candidate will combine strong software engineering fundamentals with hands-on experience in distributed data processing, large-scale storage, and LLM-based workflows. This is an exciting opportunity to work at the intersection of AI, data engineering, and product innovation, building foundational systems that enable rapid experimentation and the development of intelligent user experiences.

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Responsibilities

Design, build, and improve scalable synthetic data-generation systems that produce coherent, traceable, and reproducible datasets from structured inputs and contextual information.

Develop and maintain reliable data pipelines and workflow orchestration, applying concurrency, asynchronous execution, and resilient error handling to support distributed processing.

Design and optimize data models, storage solutions, and retrieval patterns while implementing automated validation frameworks, data-quality checks, and quantitative performance analysis.

Build, deploy, monitor, and support dependable cloud-based backend systems; evaluate technical trade-offs, troubleshoot existing code, maintain automated tests and documentation, and collaborate with engineering and product partners.

5+ years of software or data engineering experience, with demonstrated ownership of backend systems from design and implementation through ongoing support.

Strong backend programming and systems engineering skills, including maintainable services, modular components, APIs, and automated testing; experience with Python, Spring Boot, .NET, or comparable technologies.

Substantial hands-on experience designing, building, and operating data-generation systems, ETL workflows, distributed data processing, and scalable cloud-based pipelines.

Strong working understanding of LLMs, including their capabilities, limitations, common failure modes, prompting, data quality, model behavior, and evaluation of AI-powered workflows.

Experience with workflow orchestration, concurrency, asynchronous execution, streaming or event-driven architectures, and failure recovery; familiarity with technologies such as Apache Spark or Kafka is preferred.

Strong knowledge of large-scale data storage, modeling, and retrieval, including partitioning, indexing, database access patterns, cloud object storage, and data lake architectures. xrbllit

Strong quantitative analysis, troubleshooting, and communication skills, with practical cloud engineering experience and the ability to work independently, document solutions, validate system behavior, and collaborate effectively across teams.