Lead AI Data Engineer
HybridBrno, South Moravian, Czechia or Krakow, Łódź Voivodeship, Republic of Poland
Job Summary
Design, build, and maintain scalable data pipelines that support AI, machine learning, and agent-based solutions. Develop connectors across enterprise applications, databases, SaaS platforms, and knowledge repositories to enable AI solutions, agents, and automations to operate on trusted information. Partner with AI Solution Architects, Data Scientists, and business stakeholders to understand data requirements for AI use cases, designing architectures for agentic workflows, Retrieval-Augmented Generation, and semantic search. Establish standardized patterns for data ingestion, transformation, and access while resolving data quality, fragmentation, and metadata challenges. Implement monitoring and observability mechanisms to ensure data reliability and compliance with security and governance policies. Support the transition of AI solutions from pilot initiatives to enterprise-scale production deployments. Work within a hybrid model in Poland, contributing to the AI Center of Excellence strategy to transform data readiness into a measurable organizational capability.
Required Qualifications
- Bachelor's/ Master's degree in Computer Science, Information Systems, Data Engineering, Software Engineering, or a related field.
- 5+ years of experience in data engineering, integration engineering, or data platform development.
- Experience designing and building enterprise-scale data pipelines using modern cloud and data platform technologies.
- Strong understanding of data modeling, ETL/ELT processes, APIs, event-driven architectures, and data integration patterns.
- Experience working with both structured and unstructured data sources.
- Proficiency with SQL and at least one modern programming language such as Python, Java, or Scala.
- Experience working with AWS cloud platform and kubernetes
- Experience supporting AI, machine learning, generative AI, or agentic AI solutions.
- Familiarity with vector databases, embeddings, semantic search, and Retrieval-Augmented Generation (RAG) architectures.
- Experience with data governance, metadata management, data catalogs, and data lineage tools.
- Understanding of data privacy, security, and responsible AI principles.
- Experience with modern orchestration and integration tools such as Airflow, Databricks, Snowflake, Azure Data Factory, Kafka, or similar platforms.
- Experience building enterprise APIs and reusable data services.
- Knowledge of M365 Copilot, Copilot Studio, Azure AI Foundry, AWS Bedrock, or similar AI ecosystems.
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