AI Data Engineer

Salary: $85,000 - 125,000 per year Requirements: At least 5 years of software or data engineering experience, or equivalent experience building systems with languages and frameworks such as Python, Spring Boot, or .NET, including ownership through implementation and support. Strong backend programming and systems engineering skills, including designing maintainable services, modular components, APIs, and automated tests. Deep hands-on experience designing, building, and operating data-generation, ETL, and distributed data-processing workflows, including scalable cloud-based pipelines. Strong working knowledge of LLM capabilities, limitations, common failure modes, data quality, prompting, evaluation, and model behavior in AI-powered systems. Working knowledge of orchestration, concurrency, asynchronous execution, streaming or event-driven architectures, and failure recovery. Strong understanding of large-scale data storage, modeling, and retrieval, including partitioning, indexing, access patterns, and storage trade-offs. Strong quantitative analysis and troubleshooting skills, including validating data and system behavior, interpreting performance measurements, and making evidence-based engineering decisions. Practical cloud engineering experience and the ability to work independently in an existing codebase and turn evolving requirements into maintainable solutions. Clear technical communication, collaboration, documentation, and operational reliability practices. Experience with Python is preferred. Experience integrating LLMs through AI platforms, frameworks, or APIs, and building or evaluating production-oriented AI workflows is preferred. Experience evaluating LLM output, defining quality criteria, and building automated checks is preferred. Experience with distributed processing, streaming or event-driven systems, and workflow orchestration tools such as Apache Spark, Kafka, or equivalent technologies is preferred. Familiarity with relational or NoSQL databases, cloud object storage, and data lake architectures is preferred. Experience with observability, performance tuning, deployment automation, and data-quality validation frameworks is valuable. Portfolio or work samples are optional. Responsibilities: Design, build, and improve synthetic data-generation systems, assessing existing approaches and trade-offs and developing scalable, maintainable workflows that turn structured inputs and persona and organizational context into coherent, traceable, 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 approaches, and retrieval patterns for large data volumes and efficient access. Develop validation frameworks and systems, define quality criteria, automate checks, and use quantitative analysis and profiling to assess data quality, investigate system behavior, and improve performance and reliability. Apply cloud engineering practices to build, deploy, monitor, and support dependable systems while 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 support ongoing ownership. Own technical work from assessment and design through implementation and ongoing support, working in existing codebases and translating evolving requirements into maintainable solutions. Collaborate with engineers, researchers, designers, and product partners to develop reliable systems and improve the synthetic data-generation system. Technologies: AI Backend Cloud Copilot ETL Support Kafka LLM NoSQL Python Spark Spring Spring Boot ASP.NET Excel More: We are recruiting for our client, who is seeking an experienced AI Data Engineer 3 for a role based in Redmond, Washington. The position sits in a people-centered, culture-first team working with designers, product partners, engineers, strategists, and writers to create modern experiences that make intelligent systems clear, trustworthy, and human. The team explores innovative technology through rapid prototyping, model tuning and evaluation, data experiments, and AI-focused work, with a current emphasis on Copilot in the M365 Enterprise environment. Our team brings together people from a range of disciplines and values curiosity, open contribution, experimentation, constructive feedback, adaptability, and strong engineering practices. last updated 42 week of 2026