Python programmer állások XIII. kerületben (it programozás, fejlesztés)

• The CloudOps Engineer is responsible for availability for cloud platforms
• Respond, troubleshoot and resolve production alarms
• Communicate and troubleshoot operational issues supporting a complex environment
• Respond to cloud service outages and manage incident tickets

• Technology architect with T Cloud Public hands-on experience
• Deep expertise in public cloud architecture
• Strong understanding of VPC networking
• Strong understanding of IAM & federation
• Strong understanding of cloud security architecture
• Strong understanding of Kubernetes & container platforms

• Design, build, and deploy production-grade agentic AI and automation solutions that collect, validate, and quality-assure data at scale
• Engineer multi-agent orchestrations, data pipelines, MCP integrations, and API connections that power autonomous, end-to-end data workflows

• Service-level analysis of modular cloud software
• Mapping APIs, configuration flows, service dependencies, database usage, and operational assumptions of OpenStack-style services and surrounding components

• Design, deploy, and manage scalable cloud and hybrid infrastructure environments with a primary focus on automation, reliability, and security
• Build and maintain Infrastructure-as-Code solutions using Terraform and other automation tools to provision and manage AWS and on-premises resources

• 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

• Tesztmenedzsment feladatok vezetése a teljes tesztfolyamat szakmai kísérésével az igényfelméréstől a riportolásig
• Követelményanalízis fejlesztőkkel, üzemeltetőkkel és más érintettekkel közösen
• Tesztstratégiák tervezése, tesztelési lépések meghatározása és követése

• Design and implement automation for storage provisioning, capacity management, and operational workflows using Python, Bash, and IaC tooling
• Drive reduction of manual toil through scripted workflows and self-service enablement

• Working with cutting edge technology enabling cloud journey
• Managing infrastructure as code for core platform layer including Kubernetes clusters, message broker backends, and infrastructure tooling

• Data pipeline development
• Data integration
• Data warehousing
• Data modelling
• Data quality and governance
• Performance optimization
• Collaboration with data scientists and analysts
• Technology selection
• Security and compliance
• Monitoring and troubleshooting

• Development of advanced AI solutions
• Collaboration across divisions to design, develop, and deploy machine learning models and AI applications
• Working with state-of-the-art AI technologies including generative AI, natural language processing, and computer vision

• Komplex üzleti folyamatok végpontok közötti automatizálása Java és Spring Boot alapokon
• Automatizációs megoldások fejlesztése UI és API szinten Selenium, LeanFT és hasonló eszközök bevonásával
• AI-alapú prototípusok, belső eszközök és automatizációk fejlesztése kontrollált vállalati környezetben

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

Shaping and evolving the architecture of MDO network automation capabilities including reusable automation patterns, interfaces and technical guidelines...

• Design and implement pipelines to ingest, clean, and transform data into Data Lakes and Data Warehouses
• Active monitoring and optimization of pipelines for performance and cost
• Configuration of pipeline deployments and management of environment settings using IaC tools and CI/CD

• Lead AI-assisted software engineering activities for Meridian by designing repeatable SDLC patterns
• Anchor the engineering approach for OpenStack-derived services, repository-scale understanding, and sovereign software handover readiness

• Design and build secure infrastructure foundation for AI-assisted software development
• Operate Kubernetes environments hosting AI engineering tools and services
• Implement GitOps and Infrastructure as Code patterns for provisioning and configuration

• Establish data feeds with custodian banks, data vendors, and other service providers
• Support senior leadership, clients, and the broader Data Team in executing analyses and providing data-driven answers to questions
• Fully automate and centralize existing data integration processes

• Build, operate, and improve production data pipelines using Python, Airflow, Kubernetes, and related technologies
• Design scalable data pipelines and data models for growing data volumes and processing needs

• Writing clean, maintainable Python code and automation scripts (bash/shell)
• Building CI/CD pipelines and deployment automation for AI systems
• Creating infrastructure as code and configuration management solutions (terraform)
• Developing APIs, microservices, and event-driven architectures