Senior Data Engineer - AI Infrastructure
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Key skills for this role
About the Role
Kraken seeks a Senior Data Engineer for its AI Infrastructure team to own and evolve streaming data pipelines, design feature stores, and implement streaming systems using RisingWave, Apache Flink, or Kafka Streams.
Key Skills for This Role
Responsibilities
- Own and evolve streaming data pipelines that power live inference and real time model serving across Kraken's AI infrastructure
- Design and build feature stores that serve low latency, high reliability features to production ML models
- Implement and maintain streaming systems using RisingWave, Apache Flink, or Kafka Streams, selecting the right tool for the workload
- Partner with ML engineers and AI infra teams to define data contracts, feature schemas, and pipeline SLAs
- Drive pipelines toward real time where batch exists today reducing latency from hours to seconds
- Ensure data quality, observability, and auditability across all streaming and feature engineering systems
- Contribute to inference pipeline tooling where data engineering and model serving intersect
- Evaluate emerging streaming and feature store technologies and shape the team's technical roadmap
Requirements
- 5+ years in data engineering with at least 2 years focused on streaming systems in production
- Hands on experience with RisingWave, Apache Flink, Kafka Streams, or comparable stream processing frameworks
- Strong understanding of feature store design — online/offline consistency, point in time correctness, low latency serving
- Experience building data pipelines that feed production ML models or inference systems
- Proficiency in Python and/or Scala; SQL fluency required
- Familiarity with data quality frameworks, pipeline observability, and SLA ownership
- Comfortable operating in a fast moving, ambiguous environment embedded within an AI focused team
Full Job Posting
The opportunity
- Own and evolve streaming data pipelines that power live inference and real time model serving across Kraken's AI infrastructure
- Design and build feature stores that serve low latency, high reliability features to production ML models
- Implement and maintain streaming systems using RisingWave, Apache Flink, or Kafka Streams, selecting the right tool for the workload
- Partner with ML engineers and AI infra teams to define data contracts, feature schemas, and pipeline SLAs
- Drive pipelines toward real time where batch exists today reducing latency from hours to seconds
- Ensure data quality, observability, and auditability across all streaming and feature engineering systems
- Contribute to inference pipeline tooling where data engineering and model serving intersect
- Evaluate emerging streaming and feature store technologies and shape the team's technical roadmap
What You Bring
- 5+ years in data engineering with at least 2 years focused on streaming systems in production
- Hands on experience with RisingWave, Apache Flink, Kafka Streams, or comparable stream processing frameworks
- Strong understanding of feature store design — online/offline consistency, point in time correctness, low latency serving
- Experience building data pipelines that feed production ML models or inference systems
- Proficiency in Python and/or Scala; SQL fluency required
- Familiarity with data quality frameworks, pipeline observability, and SLA ownership
- Comfortable operating in a fast moving, ambiguous environment embedded within an AI focused team
Nice to haves
- Direct experience with RisingWave in production
- Exposure to inference pipeline architecture or model serving infrastructure
- Experience with feature platforms
- Crypto or fintech domain experience
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