AI Data Engineer 3 [213704]

Join a supportive environment where you can build real-world capabilities for modern AI experiences. This onsite role in Redmond, WA pairs synthetic data engineering with scalable pipelines, validation frameworks, and cloud-deployed backend services. If you enjoy turning complex inputs into coherent, traceable datasets and improving reliability through measurement and automation, you will have meaningful ownership from design through operations.

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

What you’ll do

• Design, build, and evolve synthetic data-generation systems, evaluating existing approaches and trade-offs while producing scalable and maintainable workflows.
• Translate structured inputs, persona, and organizational context into coherent, traceable, and reproducible datasets.
• Build and operate scalable pipelines and orchestration, using concurrency, asynchronous execution, and resilient failure handling where appropriate.
• Design and evolve data models, storage strategies, and retrieval patterns for large volumes and efficient access.
• Develop 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, balancing scalability, operational simplicity, and cost.
• Evaluate technical trade-offs, communicate progress and risks, and collaborate on practical solutions while maintaining clear documentation, automated tests, and guidance for ongoing ownership.

What you bring

• 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.
• Working understanding of large language models (LLMs), including capabilities, limitations, common failure modes, and how data quality, prompting, evaluation, and model behavior impact AI systems.
• Knowledge of orchestration, concurrency, asynchronous execution, and streaming or event-driven architectures, including recovery from failures.
• Strong expertise in 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 and make evidence-based decisions.
• Practical cloud engineering experience and ability to work independently in an existing codebase.
• Clear technical communication, effective collaboration, and disciplined documentation and operational reliability.

Technologies you’ll work with

• Python, Spring Boot, .NET
• ETL, LLMs
• Apache Spark, Kafka
• Relational databases, NoSQL databases
• Cloud object storage, data lake architectures

Benefits

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

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 LLM output evaluations, including defining quality criteria and building automated checks.
• Experience with distributed processing, streaming or event-driven systems, and orchestration tools such as Apache Spark, Kafka, or equivalents, along with 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.