Python developer állások VIII. kerületben (it programozás, fejlesztés)

• Building end-to-end web solutions from UI to backend
• Leading and contributing to development efforts across the full stack
• Design and implementation of web applications using modern frameworks and best practices
• Translating business and user needs into intuitive, scalable technical solutions

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

Az Agentic AI Platform célarchitektúrájának meghatározása és fenntartása (agent runtime, orchestration, tool integration, MCP szerverek, modellhozzáférés, RAG, memória, guardrail-ek, observability, fejlesztői enablement)...

• 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

• Managing day-to-day operations of Databricks data pipelines, including monitoring, troubleshooting, and performance tuning
• Maintaining Delta Lake data layers (bronze / silver / gold), ensuring data availability, consistency, and smooth pipeline execution

• Building and optimizing inference pipelines for large-scale model serving
• Working with frameworks like PyTorch, TensorRT, and vLLM to deploy models efficiently
• Implementing and optimizing ML models using techniques such as quantization, kernel fusion, and efficient batching

• 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

• Design, implement, and optimize compiler passes and transformations using LLVM/Clang or GCC
• Build and maintain robust toolchains
• Support new and complex architectures in compiler backends
• Work on architecture-agnostic and IR-level optimizations
• Work on silicon specific optimizations

• Együttműködés belső ügyfelekkel és külső fejlesztőcsapatokkal adatvezérelt működés és döntéshozatal elősegítése érdekében
• Strukturált és nem strukturált adatok feldolgozása, azokból információ keresése, kinyerése, feldolgozása, szolgáltatása (elsősorban backend fejlesztések)

• 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

• Owning and delivering complex C++ features across the full development lifecycle [design, implementation, review, maintenance]
• Driving code quality improvements and reducing technical debt through refactoring and pragmatic modernization

• Building and maintaining simulation environments for mobile robotic perception and navigation
• Developing and integrating robotic behaviours and learning pipelines for reinforcement learning and motion planning in complex outdoor environments

• 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