At a Glance
- Tasks: Build and maintain data pipelines for real-time AI model serving and streaming systems.
- Company: Join a leading AI infrastructure team powering intelligent agents at scale.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic, fast-paced environment with a focus on innovation and collaboration.
- Why this job: Be at the forefront of AI technology and make a significant impact on real-world applications.
- Qualifications: 5+ years in data engineering with expertise in streaming systems and Python or Scala.
The predicted salary is between 80000 - 98000 £ per year.
The AI Infrastructure team builds and operates the production systems that power intelligent agents at scale. This team sits at the foundation of the agent platform, ensuring that model inference, orchestration, and execution layers are reliable, observable, and performant under real-world load.
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.
Qualifications
- 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.
- Direct experience with RisingWave in production.
- Exposure to inference pipeline architecture or model serving infrastructure.
- Experience with feature platforms.
- Crypto or fintech domain experience.
Data Engineer - AI Infrastructure employer: Payward, Inc.
As a leading player in the AI infrastructure space, our company offers an exceptional work environment where innovation thrives and collaboration is key. Employees benefit from a culture that prioritises professional growth, with ample opportunities to advance skills in cutting-edge technologies like RisingWave and Apache Flink. Located in a vibrant tech hub, we provide a dynamic atmosphere that encourages creativity and supports a healthy work-life balance, making it an ideal place for passionate data engineers to make a meaningful impact.
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We think you need these skills to ace Data Engineer - AI Infrastructure
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Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
