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TechBiz Globaltoday

Senior AI Data Engineer

On-site · Kraków, Lesser Poland, Poland or Warsaw, Mazovia, Poland

Type
Full Time
Level
Senior Level
Education
Bachelors Degree
Company size
Unknown

Job Summary

Senior AI Data Engineer responsible for designing, building, and scaling robust ETL/ELT pipelines for AI workloads; transforming unstructured data into structured, vectorized formats for LLMs; maintaining data-to-model lifecycle and real-time feature pipelines; integrating with Kafka and event-driven systems; managing Feature Stores and data quality, lineage, and governance across on-premises and cloud data platforms; enabling vector DB storage for high-performance AI search; collaborating with data scientists, ML engineers, software engineers, and stakeholders to deliver scalable AI data solutions; requires deep expertise in Python/SQL, real-time streaming (Kafka/Flink/Spark), orchestration tools (Airflow/dbt/Prefect), vector databases, and cloud/Kubernetes environments.

Required Qualifications

  • Bachelor's degree in Computer Science, Software Engineering, Information Systems, or related technical field. Equivalent practical experience will also be considered.
  • 10+ years of experience in Data Engineering or Backend Engineering with a strong focus on data platforms and pipelines.
  • 2+ years of hands-on experience supporting AI/ML data pipelines, including data preparation for machine learning and generative AI applications.
  • Expert-level proficiency in Python and SQL; experience with Java or Scala is an advantage.
  • Strong experience building and maintaining real-time data streaming solutions using Apache Kafka, Flink, or Spark Streaming.
  • Hands-on experience with modern data orchestration and transformation tools such as Airflow, dbt, and Prefect.
  • Experience working with Vector Databases and Feature Stores to support AI and machine learning workloads.
  • Strong knowledge of cloud-based data services on AWS, Azure, or GCP, including services such as Glue, Kinesis, Data Factory, or Dataflow.
  • Experience deploying and managing data workloads in Kubernetes (K8s) environments.
  • Proven experience handling sensitive data within regulated industries such as Fintech, Healthcare, or other compliance-driven environments.
  • Strong understanding of data quality, governance, security, and privacy best practices.
  • Excellent problem-solving skills and the ability to collaborate effectively with cross-functional engineering, data, and AI teams.
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TechBiz Global

Senior AI Data Engineer

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