Python programozó diákmunka állások Budapesten (it programozás, fejlesztés)

• Architect scalable inference services, APIs, and backend components for model-driven and LLM-powered product features
• Productionize AI and ML workflows with strong MLOps practices
• Define and implement evaluation frameworks for model quality, system reliability, latency, and cost

• Design, build, and ship production retrieval systems
• Own vector store selection and operation including scalability, latency, reliability, cost, and multi-tenant design
• Build and operate indexing and refresh pipelines including chunking, embedding generation, backfills, deletes, and versioned indices

• Design of complex and scalable data solutions on the AWS cloud platform
• Development and implementation of complex and scalable data solutions on the AWS cloud platform
• Design and building of data pipelines to ensure efficiency and reliability
• Optimization of data pipelines

• Design and run Kubernetes environments optimized for AI inference, retrieval, experimentation, and agent execution in secure or isolated settings
• Deploy and operate open-source or open-weight model stacks, model gateways, vector databases, and supporting platform components

• AI-alapú rendszerek és alkalmazások architektúrájának tervezése és fejlesztése
• Microservice és event-driven architektúrák tervezése és technikai irányvonal meghatározása
• Architekturális döntések meghozatala, beleértve a megfelelő technológiai és tervezési megoldások kiválasztását

• Design, implement, and maintain scalable BI and data processing solutions
• Develop and operate ETL pipelines for customer, booking, and product data
• Integrate data from various file- and REST-based Magenta APIs
• Generate and deliver reports and datasets for multiple downstream consumers

• Providing technical assistance and guidance to data engineering and machine learning community
• Taking ownership of difficult technical problems
• Participating in the vision for data engineering through documents and conversations
• Getting buy-in from stakeholders and leadership

• Design, development, and optimization of core ML models in Computer Vision and Deep Learning
• Development of efficient, scalable training and inference pipelines
• Mentoring junior ML engineers, code review and sharing best practices

• 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

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

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

• Architect and lead the development of large-scale ML systems and model pipelines
• Introduce new ML systems such as traffic sign detection, building facade detection, and other computer vision-based solutions
• Define and drive the ML technical roadmap in alignment with company objectives

• 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

• 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