Data Integration Engineer GenAI

Role description

Role: Data Integration Engineer GenAI

Krakow, Poland(Hybrid, 3 days work from office in a week)

6 Months contract

Experience 5 to 7 Years

Responsibilities

• 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

To be successful in this role you should have

• 5 to 7 years of experience in Data Engineering Data Integration Backend Engineering or Software Engineering
• Strong Python development experience
• Handson experience with FastAPI and Microservices Architecture
• Experience building enterprise data integration pipelines
• Strong understanding of API design and backend service development
• Experience with MongoDB and NoSQL databases
• Experience with PostgreSQL and relational databases
• Knowledge of Vector Databases and Vector Search concepts
• Experience with Hadoop based data platforms
• Experience with Starburst or Data Virtualization technologies
• Experience with Google BigQuery
• Handson experience with Google Cloud Platform GCP
• Exposure to Azure Cloud services
• Understanding of Retrieval Augmented Generation RAG architectures
• Knowledge of Context Engineering and AI data retrieval patterns
• Experience integrating AIML solutions with enterprise data sources