Data Integration Engineer GenAI

We are looking for a Data Integration Engineer (GenAI) to design, build, and maintain scalable data integration pipelines and high-performance microservices for Generative AI platforms. In this role, you will leverage Python and FastAPI to build real-time orchestration services, connect legacy enterprise data stores (Hadoop, Starburst, PostgreSQL, MongoDB) with Google Cloud Platform (GCP / BigQuery), and deploy low-latency Retrieval-Augmented Generation (RAG) solutions using vector stores like PGVector.

If you have a strong background in data engineering or Python backend development, hands-on experience with GCP and vector search, and want to build enterprise-grade AI data retrieval systems in a hybrid Kraków environment, we’d love to talk to you.

What you can expect from us?

• Form of employment: B2B contract
• Hybrid Work Model: Balanced setup with 2-3 days per week in a modern Kraków office and core working hours from 9:00 AM to 5:00 PM CET
• High-Impact Enterprise Environment: Direct involvement in modernizing critical technology ecosystem, combining global banking stability with modern engineering standards
• Cutting-Edge Tech Stack & AI Innovation: Hands-on experience with Python, FastAPI, RAG, PGVector, microservices, and Google Cloud Platform (GCP & BigQuery)
• Global Collaboration & Engineering Culture: Work alongside AI engineers, platform engineers, and international stakeholders within a collaborative, cross-border delivery model

Employment agency entry number 47

this job offer is intended for people over 18 years of age

What will your tasks involve?

• Design and develop data integration solutions to support Generative AI platforms and applications
• Build agents that source and retrieve data from multiple enterprise systems for GenAI use cases
• Develop scalable data transfer pipelines to enable efficient movement and processing of data
• Design and implement Python based microservices using FastAPI
• Integrate structured and unstructured data sources into AI and analytics platforms
• Build and support Retrieval Augmented Generation RAG solutions and context aware AI services
• Develop data access and retrieval services leveraging vector databases and memory stores
• Work with cloud native services across Google Cloud Platform GCP and Azure
• Design solutions that support data virtualization data governance and enterprise scale AI workloads
• Collaborate with AI engineers platform engineers and data teams to enable GenAI solutions
• Ensure solutions are scalable secure reliable and production ready
• Contribute to evaluation frameworks context engineering and AI model performance improvements

What we expect from you?

• Core Experience: 5-7 years of experience in Data Engineering, Data Integration, or Backend Engineering
• Python & Microservices: Strong hands-on Python development experience, including FastAPI and microservices architecture
• Data Integration & Virtualization: Proven experience building enterprise data pipelines, with exposure to Big Data engines like Hadoop, Starburst (Trino), and data virtualization
• Databases & Cloud: Solid knowledge of relational and NoSQL databases (PostgreSQL, MongoDB) and hands-on experience with Google Cloud Platform (GCP & BigQuery)
• GenAI & Vector Stores: Understanding of Retrieval-Augmented Generation (RAG) architectures, Context Engineering, and experience working with Vector Databases (such as PGVector)
• Production Standards: Ability to deliver scalable, secure, reliable, and production-ready enterprise data solutions
• Collaboration: Strong problem-solving, data modeling, and communication skills to collaborate with cross-functional AI, platform, and data teams