AI Data Engineer

Aquent Talent is hiring an AI Data Engineer 3 for an onsite role in Redmond, WA
. The position focuses on building the data foundation for modern AI experiences, including synthetic data-generation systems, scalable pipelines, validation frameworks, and cloud-deployed backend services. You will work in a supportive environment with flexible work options and tools to thrive both personally and professionally
.

• Design, build, and evolve synthetic data-generation systems
  , evaluating existing approaches and trade-offs while creating scalable, maintainable workflows. Translate structured inputs, persona, and organizational context into coherent, traceable, and reproducible datasets.
• Build and operate scalable pipelines and orchestration
  , applying concurrency, asynchronous execution, and resilient failure handling where needed.
• Design and improve data models
  , storage strategies, and retrieval patterns to support large volumes and efficient access.
• Develop validation frameworks that define quality criteria and automate checks. Use quantitative analysis and profiling to assess data quality, investigate system behavior, and improve performance and reliability
  .
• Use cloud engineering practices to build, deploy, monitor, and support dependable systems, balancing scalability, operational simplicity, and cost.
• Evaluate technical trade-offs, communicate progress and risks, and collaborate on practical solutions. Maintain clear documentation,
  automated tests
  , and guidance that supports ongoing ownership.

• 5+ years of software or data engineering experience, or equivalent significant experience building systems with coding in languages and frameworks including, but not limited to,
  Python, Spring Boot, and .NET
  , with demonstrated ownership through implementation and support.
• Strong backend programming and systems engineering skills, including maintainable services, modular components, APIs, and automated tests.
• Deep experience designing, building, and operating data generation
  , ETL
  , and distributed data-processing workflows, including creating, scaling, and maintaining cloud-based pipelines
  .
• Strong working understanding of large language models (LLMs), including capabilities, limitations, common failure modes, and how data quality, prompting, evaluation, and model behavior influence AI-powered systems.
• Working knowledge of orchestration
  , concurrency
  , asynchronous execution, streaming or event-driven architectures, and recovery from failures.
• Strong knowledge of large-scale data storage
  , modeling, and retrieval, including partitioning, indexing, access patterns, and storage trade-offs.
• Strong quantitative analytical and troubleshooting skills to validate data and system behavior, interpret performance measurements, and make evidence-based engineering decisions.
• Practical cloud engineering experience and ability to work independently in an existing codebase.
• Clear technical communication, effective collaboration, and a disciplined approach to documentation and operational reliability.

• Python
  , Spring Boot
  , .NET
• ETL
  , LLMs
• Apache Spark
  , Kafka
• Relational databases, No
  
  SQL databases
• Cloud object storage, data lake architectures

• Experience with Python
  .
• Experience integrating LLMs through AI platforms, frameworks, and APIs, including building or evaluating production-oriented AI workflows.
• Experience designing and running LLM output evaluations
  , including defining quality criteria and building automated checks.
• Experience with distributed processing, streaming or event-driven systems, and workflow orchestration tools such as Apache Spark
  , Kafka
  , or equivalent technologies, plus familiarity with relational or No
  
  SQL databases, cloud object storage, or data lake architectures.
• Experience with observability
  , performance tuning, and deployment automation
  .
• Ability to work independently within an existing codebase and translate evolving requirements into maintainable solutions.
• Strong technical communication, documentation, collaboration, adaptability, and problem-solving skills.
• Experience creating automated tests and data-quality validation frameworks
  .

Compensation: USD 70 - 77 per hour.
Experience requirement: 5+ years.
Location: Redmond, WA (onsite).