Senior Data Engineer

Senior Data Engineer – AI Systems
Employment Type: Full-time
Company: Permute (www.permute.ai http://www.permute.ai)

OVERVIEW

Permute is seeking a Senior Data Engineer to design, build, and operate the data infrastructure that powers production AI systems. This role is for builders who move quickly, think clearly in ambiguous situations, and take ownership of turning messy data and evolving requirements into reliable, scalable systems.

We care as much about how you think and build as we do about your background. The ideal candidate is someone who can reason through new data problems, prototype quickly, and build robust pipelines and infrastructure in a fast-moving startup environment.

RESPONSIBILITIES

• Design and build scalable data pipelines and infrastructure supporting AI and ML systems
• Develop and maintain data ingestion, transformation, and storage systems for large-scale datasets
• Build data platforms that support LLM systems, agent workflows, and model evaluation
• Develop tooling for data quality, validation, monitoring, and observability
• Collaborate with engineering, product, and ML teams to deliver data systems that power AI features
• Design systems that enable efficient experimentation, training, and evaluation of models
• Optimize performance, reliability, and scalability of data infrastructure
• Operate effectively in ambiguous, rapidly evolving startup environments

REQUIRED QUALIFICATIONS

• Strong background in data engineering and distributed systems
• Experience building scalable data pipelines and data platforms
• Strong foundation in algorithms, statistics, and data modeling
• Proficiency with Python and modern data infrastructure tools
• 5+ years building and operating systems in AWS environments

PREFERRED BACKGROUND

• Degree in Mathematics, Physics, Computer Science, or a related technical field

Experience with:

• End-to-end production data platforms
• Streaming and batch data processing systems
• Data infrastructure supporting ML training, MLOps, evaluation, and deployment
• Data quality, monitoring, and observability systems
• Infrastructure supporting AI or LLM-powered applications