AI Data Engineer 3 [213704]

Aquent Talent is hiring an AI Data Engineer 3 for an onsite role in Redmond, WA. In this position, you will help build synthetic data-generation systems, scalable data pipelines, and validation frameworks, then support cloud-deployed backend services that enable modern AI experiences. The work blends data engineering, LLM quality and evaluation, and production-minded cloud engineering.

You will partner across teams to turn structured inputs, persona, and organizational context into coherent, traceable, and reproducible datasets. You will also contribute to model tuning and data experiments, including LLM-related quality and evaluation workflows, with a focus on reliability, performance, and maintainable engineering practices.

What you’ll do

• Design, build, and evolve synthetic data-generation systems, evaluating existing approaches and technical trade-offs while creating scalable, maintainable workflows.
• Develop and operate scalable pipelines and orchestration, using concurrency, asynchronous execution, and resilient failure handling where appropriate.
• Design data models, storage approaches, and retrieval patterns to support large data volumes and efficient access.
• Create and evolve validation frameworks that define quality criteria, automate checks, and use quantitative analysis and profiling to assess data quality and system behavior.
• Apply cloud engineering practices to build, deploy, monitor, and support dependable systems while balancing scalability, operational simplicity, and cost.
• Evaluate trade-offs, communicate progress and risks, collaborate on practical solutions, and maintain clear documentation, automated tests, and guidance for ongoing ownership.

What you’ll bring

• 5+ years of software or data engineering experience (or equivalent), with demonstrated ownership through implementation and support using languages and frameworks such as Python, Spring Boot, and .NET.
• Strong backend programming and systems engineering skills, including maintainable services, modular components, APIs, and automated tests.
• Deep experience building and operating data generation, ETL, and distributed data-processing workflows, including creating and maintaining cloud-based pipelines.
• Strong working understanding of large language models (LLMs), including capabilities, limitations, failure modes, and how data quality, prompting, evaluation, and model behavior impact AI-powered systems.
• Working knowledge of orchestration, concurrency, asynchronous execution, streaming or event-driven architectures, and failure recovery.
• Strong knowledge of large-scale data storage, modeling, and retrieval, including partitioning, indexing, and access pattern trade-offs.
• Strong quantitative analytical and troubleshooting skills to validate data and interpret performance measurements for evidence-based decisions.
• Practical cloud engineering experience and the ability to work independently in an existing codebase.
• Clear technical communication, effective collaboration, and a disciplined documentation and operational reliability approach.

Tools you’ll use

Python, Spring Boot, .NET, ETL, LLMs, Apache Spark, Kafka, relational databases, NoSQL databases, cloud object storage, data lake architectures

Flexible work and support

• Flexible work options
• Supportive environment
• Tools to thrive both personally and professionally

Compensation: USD 70 - 77 per hourly.

What will set you apart

• Experience with Python.
• Experience integrating LLMs via AI platforms, frameworks, and APIs, including building or evaluating production-oriented AI workflows.
• Experience designing and running evaluations of LLM output, 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 NoSQL 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.