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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Python Developer - Generative AI / RAG - **Company:** Qualysoft GmbH Python Developer - Generative AI / RAGQualysoft GmbH - **Location:** Wien, Austria (Remote available) - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Unit Testing, Code Review, Databases, Continuous Integration, Data Transformation, Software Debugging, Python (Programming Language), PostgreSQL, Meta-Data Management, NoSQL, Software Architecture, E2e Testing, Search Technologies, Software Engineering, Data Logging, Data Processing, Retrieval-Augmented Generation, Large Language Models, Generative AI, Backend, Git, Fastapi, Integration Tests, Information Technology, Data Analytics, GPT, Software Version Control - **Published:** September 25, 2026 - **Apply:** https://devjobs.at/job/5d55665c28a5ff491ab1afded446c6a5 ## About the Role * Completed degree in computer science, business informatics, data science, Artificial Intelligence or a comparable qualification, * Very good knowledge of Python and modern Python software-development practices * Very good understanding of RAG architectures, Large Language Models and Generative AI applications * Sound understanding of APIs, integration patterns and backend architectures * Knowledge of clean code principles, software architecture and maintainable application design * Strong data-driven and analytical mindset * Ability to evaluate technical solutions based on measurable quality, performance and business requirements * Very good written and spoken English skills * Structured, independent and solution-oriented way of working * Strong communication and collaboration skills, * At least 5+ years of professional experience in software development with Python * Proven experience in the development of APIs, backend services and data-processing applications * Practical experience in the development and implementation of Retrieval-Augmented Generation solutions * Experience with document ingestion, document parsing, preprocessing, chunking and metadata management * Experience with embeddings, semantic search, vector search, retrieval and reranking * Experience with vector databases such as Qdrant, Weaviate, Pinecone, Milvus, Chroma or PostgreSQL with pgvector * Experience with RAG and LLM frameworks such as LangChain, LlamaIndex or comparable technologies * Experience with relational and/or NoSQL databases * Experience with Git and professional version-control workflows * Experience with unit testing, integration testing and automated software testing * Experience with logging, monitoring, debugging and production software operations ## Description * Development and implementation of scalable Python applications, backend services and AI-based solutions * Design, development and optimization of complete Retrieval-Augmented Generation pipelines * Implementation of document ingestion and data-processing workflows * Development of suitable document parsing, preprocessing, chunking and metadata strategies * Generation, management and optimization of embeddings * Implementation of semantic search, vector search and hybrid search solutions * Integration and management of vector databases and retrieval systems * Development and optimization of retrieval and reranking mechanisms * Implementation of prompt construction, prompt templates and context-management strategies * Integration of Large Language Models for reliable and context-based answer generation * Implementation of citation handling, source attribution and traceability mechanisms * Development and integration of APIs and backend interfaces * Integration of AI applications into existing IT, data, database and cloud landscapes * Design and implementation of data-driven evaluation processes for RAG and AI solutions * Analysis and improvement of retrieval quality, answer quality, relevance and system performance * Development of automated unit, integration and end-to-end tests * Implementation of logging, monitoring, error handling and observability mechanisms * Creation of technical documentation and development standards * Application of professional software-development practices, including version control, code reviews and CI/CD * Collaboration with AI architects, data scientists, developers, business departments and other technical stakeholders * Support of AI use cases from technical conception through implementation to productive operation ## Related Videos - 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