Python programozó diákmunka állások Budapesten (it programozás, fejlesztés)

• Implementation and improvement of business objects for mobile network infrastructure in backend systems up to the API layer
• Development and maintenance of business objects including data models, backend logic, and APIs

Design and build knowledge tracing and longitudinal learner models that support mastery and progression features surfaced to customers...

• On-premise IT infrastruktúra üzemeltetése, támogatása és folyamatos fejlesztése
• Linux alapú rendszerek adminisztrációja
• Kubernetes klaszterek adminisztrációja
• Middleware komponensek adminisztrációja
• Automatizációs feladatok megvalósítása és karbantartása

• Dashboard development with Power BI to visualize sustainability data and metrics
• Data integration from internal databases, external APIs, and third-party platforms
• Client reporting collaboration with sustainability team for customized visual analytics

• LLM/GenAI architektúrák és agentek tervezése
• Ügyfelek fejlesztési igényeinek felmérése, átlátása és rendszerezése
• Specifikációk és rendszertervek készítése
• Prototípusok és PoC-ok létrehozása
• AI-megoldások jó gyakorlatai szerinti kialakításában való részvétel

• Build and operate multi-cluster containerized environments across cloud platforms
• Create end-to-end CI/CD pipelines for cloud-native applications and machine learning workloads
• Lead cost-optimization and FinOps initiatives for GPU, storage and data-egress spend

• Új technológiák bevezetése
• Termékfejlesztési igények implementálása
• Rendszertervezés
• Tesztelés
• Dokumentálás
• Kapcsolódó alkalmazás- és interfészfejlesztésekben való együttműködés a társszervezőkkel
• Minőségbiztosítási feladatok támogatása

• Development and operation of data pipelines
• Building data layers in OneDataLake (ODL) Central Data Store Consumer (CDSC)
• Balancing business and IT demands to build modern and efficient pipelines in public cloud environments
• Integration of data preparations into managed processes

• Development and enhancement of T Cloud Public platform
• Operation of the developed cloud platform
• Solving complex problems in the daily operation of a hyper-scaler's cloud backend
• Building tools to reduce occurrence of errors and improve customer experience

• Design and implement modern data architectures and lakehouse solutions
• Provide technical leadership throughout the project lifecycle
• Collaborate with business stakeholders
• Create effort estimations and provide technical expertise during the pre-sales phase
• Mentor 3-6 person project teams

• Design, build, and maintain robust CI/CD pipelines
• Manage and optimize cloud infrastructure on T Cloud Public
• Administer and troubleshoot Linux and Windows systems in production environments
• Deploy, configure, and manage containerized applications using Docker and Kubernetes

• 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

• Analysis and research of cloud-native tools and frameworks for Google Cloud Platform solutions
• Support for T-Systems Public Cloud Product Management Team in solution building and definition

• Design and ship production-grade agentic systems
• Take technical ownership of Compliance in a Box (CiaB) AI platform
• Architect and evolve AI reasoning layer and agent orchestration framework
• Build and maintain the GRC Brain and Agentic Cortex technical core
• Design and implement a 7-agent architecture

• Designing OpenStack-based network services on the T Cloud Public platform
• Operating OpenStack-based network services on the T Cloud Public platform
• Developing OpenStack-based network services on the T Cloud Public platform
• Planning automation initiatives using modern frameworks

• Architect and implement scalable AI/ML solutions on Azure or GCP
• Design and build AI solutions for sales use cases such as automated RFP/RFI ingestion, classification, and response generation
• Design and build intelligent document understanding solutions

• Design and develop scalable cloud security automation solutions across multi-cloud environments
• Build and maintain platform services using modern engineering practices and a code-first approach
• Own and enhance CI/CD pipelines that enable secure, reliable and efficient delivery

• Lead the end-to-end implementation of data pipelines and GenAI applications on Databricks
• Design and maintain CI/CD, model serving, monitoring, and evaluation frameworks for AI production reliability
• Tune complex Spark jobs and LLM inference patterns for maximum efficiency and minimum latency

• Building transformative AI capabilities from the ground up
• Working with conversational and agentic AI technologies to develop user-facing features
• Experimenting with generative AI models and techniques
• Tackling challenges at the intersection of AI and enterprise software

• Automation and database management
• Creation of technical specification documents for functional requirements
• Management and creation of automations using Alteryx, Python, SSIS
• Creation of reports and dashboards in Power BI
• Building data models and creating measures in DAX