Python developer állás, munka

Performing tasks related to the position....

• Architecture of cloud-native AI applications integrating LLMs, vector search, and real-time data
• Design of end-to-end web solutions from frontend to backend
• Implementation of RAG pipelines, model serving, and AI agents in production
• Leadership of cross-functional teams (Full-stack, AI, Data)

Project specific intro: Join a cybersecurity and AI project supporting Siemens Cybersecurity Governance, Risk & Security (CYS GRS ARC). You will work on the further development of the AIGIS System, an AI-powered platform that helps Siemens employees respond to customer cybersecurity questionnaires...

• Design, develop, and implement complex, scalable Lakehouse solutions on the Databricks platform across major cloud environments
• Design, build, and optimize modular data pipelines using Delta Lake to ensure data efficiency, reliability, and quality

• Kapcsolattartás az ügyfelekkel, igények felmérése és tanácsadás
• Azure Function App fejlesztés
• Dokumentálás
• Programozás Pythonban
• Azure infrastruktúra tervezés és karbantartás
• Rendszeres ellenőrzés, frissítés és teljesítmény optimalizálás Azure környezetben
• Fejlesztések tervezésében aktív részvétel

• Experience in different data science topics
• Independent delivery of data science projects with product-oriented mindset
• Engagement with clients to understand business objectives and translate into technical requirements
• Leading pre-sales technical discussions

• Ownership of data engineering features, architecture, and code quality
• Design, implementation, and maintenance of Databricks-based data pipelines and workflows
• Building and optimization of ETL/ELT processes using Apache Spark on Databricks

• Support the implementation and ongoing improvement of digital services and solutions for business customers
• Define and maintain development, testing, and operational standards to support continuous delivery
• Design, build, and maintain tools for deployment, monitoring, and operations

• Chunking and ingestion architecture with deterministic, validation-gated pipelines handling diverse enterprise document types with indexing and contextual enrichment
• Retrieval pipeline with vector search, BM25 hybrid retrieval, RRF re-ranking, embedding model selection and optimisation

• Resource account igénylése és összekötése namespacekkel
• Resource accountba kerülő komponensek megvalósítása RDS, APIk, S3, MSK, cognito, SES, private link endpoint és endpoint service nonprod és prod környezeteken
• RICE API kapcsolatok kialakítása