Python állások X. kerületben középiskolai végzettséggel

• 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

• Configuration of the P&P platform end to end across frontend and backend
• Tuning and optimization of forecast and attribution models to match client data and business logic
• Writing custom business logic in SQL, Python, or Go for non-standard configuration

• 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

• 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

• Operational monitoring to resolve issues reported by monitoring application
• Maintenance of availability, integrity and confidentiality of security platforms
• Upgrade, remediation and patching of underlying operating systems including Windows, Linux, ESXi, and firewalls

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

• Building and operating Kubernetes platforms on Open Telekom Cloud
• Managing Infrastructure as Code with Terraform and Terragrunt
• Implementing GitOps delivery using ArgoCD
• Operating PostgreSQL and Apache Kafka on Kubernetes
• Integrating security controls into CI/CD pipelines

• Design, build, and maintain robust CI/CD pipelines
• Manage and optimize cloud infrastructure on T Cloud Public, ensuring high availability and performance
• Administer and troubleshoot Linux and Windows systems in production environments

• Leading and mentoring a team of ML engineers
• Owning model quality end-to-end from experimentation to production-ready artifact
• Conducting regular 1-on-1s for career development and feedback
• Removing blockers and ensuring team well-being

• Definition and development of system-level tests for high-end embedded computing systems
• Automation of test execution and evaluation through scripting in a CI environment
• Increase of test coverage by extending functional examples into comprehensive test suites

• 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 the design and implementation of scalable data pipelines and Lakehouse architectures
• Act as the design authority
• Solve the hardest technical challenges including optimizing high-load streaming pipelines, debugging complex Spark jobs, and designing generic frameworks

• End-to-end penetration testing engagements across web applications, APIs, cloud environments, binary applications and internal/external infrastructure
• Simulation of real-world attacks to identify security risks

• Design and maintain automated CI/CD pipelines
• Automate provisioning and management of infrastructure using Infrastructure as Code tools
• Manage health and performance of mission-critical applications
• Implement advanced monitoring, logging, and tracing for legacy and modern services

• Improvement of repetitive processes using AI
• Organization and checking of data used by the Operations team
• Connecting everyday tools to reduce manual work
• Experimentation with AI tools, chatbots and workflows
• Troubleshooting and solving smaller operational tasks

• Industrialization of AI-assisted engineering workflows for Meridian
• Packaging repeatable patterns supporting codebase intake, dependency extraction, build triage, documentation generation, and technical evidence creation across cloud software work packages

• End-to-end penetration testing engagements across web applications, APIs, cloud environments, kubernets, binary applications and internal/external infrastructure
• Simulation of real-world attacks to identify security risks before they become threats

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

• Automatizált UI és end-to-end tesztek fejlesztése webalkalmazásokhoz
• Automatizált UI és end-to-end tesztek karbantartása webalkalmazásokhoz
• Automatizált UI és end-to-end tesztek stabilizálása webalkalmazásokhoz
• Webes tesztautomatizálási keretrendszerek használata és továbbfejlesztése

• 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