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You understand our platform architecture, existing systems and tech stack (C#/.NET, MongoDB, Docker, Kubernetes, GCP/Azure) deeply enough to independently plan and ship features....

• 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

• Providing technical expertise on Veeva’s applications, Vault Platform, APIs, and Java SDK
• Developing custom code and configuring Veeva software
• Contributing to solution design under guidance of experienced technical and migration architects
• Supporting content and data migration

• Design and develop automation frameworks across frontend, backend, desktop, API, and data-driven systems
• Establish testing patterns and best practices for long-term maintainability
• Develop and maintain automated UI and end-to-end test suites using Playwright or Cypress

Where you belong: Where you belong: ...

• Put people first and value human interaction over process.
• Embrace radical change and aren't scared of ambiguity or the unknown.
• Make a positive impact on individuals, teams, and the products they build.

• Supporting strategic implementation projects in choosing the right technologies, products, and partners (internal & external)
• Bringing in your project management expertise to ensure the project deliverables and expectations are met with internal & external technology providers

• Design, implement and continuously improve DevOps concepts, CI/CD templates, pipelines and automation for SAP landscapes (e.g. SAP BTP, S/4HANA)
• Enable reliable build, test and deployment processes across SAP Cloud and on-premise environments

• Leading the architecture discussion for the Core Platforms (CRM, Billing etc.) to deliver seamless business solutions for the Energy Retail Segment
• Collaborating with business stakeholders to understand requirements and translate them into scalable and effective technical designs

• Definition and driving of the Quality Engineering strategy
• Design, creation and maintenance of reusable test automation frameworks
• Establishment of testing standards and best practices
• Introduction and improvement of CI/CD quality gates
• Building of E2E, integration and API testing capabilities

• Own and execute the full IT audit cycle, covering risk and control management to ensure operational effectiveness, system reliability, and compliance with all relevant regulations and directives
• Support the definition of internal audit scope and contribute to the development of annual audit plans

End-to-end build-out of the COO data warehouse – from data modeling and pipeline development to the integration of relevant source systems and the delivery of usable data products...

End-to-end build-out of the COO data warehouse – from data modeling and pipeline development to the integration of relevant source systems and the delivery of usable data products...

• Collaborate closely with your Tech Lead on architecting new features and seeing them through to completion.
• Design, develop, test, deploy and maintain software in a thriving environment.

• Manage group-wide AI security initiatives and governance
• Consult local tech and security teams on information security and operational resilience
• Evaluate AI / information security risk for the group
• Strategically analyse threat and market intelligence

• Vállalati IT infrastruktúra architektúra megtervezése, fejlesztése és dokumentálása
• Infrastruktúrához kapcsolódó architektúra-elvek, standardek és technológiai irányok kialakítása és képviselete
• IT infrastruktúrára hatással lévő projektek folyamatos figyelemmel kísérése

Drive the Cloud Infrastructure Security function roadmap in alignment with the Product Manager, Tech Lead, and Engineering Manager....

• Ownership and evolution of Cypress and TypeScript end-to-end automation framework
• Integration of automated test suites into CI/CD pipelines
• Research and implementation of load and performance testing strategies
• Application of AI-assisted tools for test generation and failure triage

• Design necessary data models and transformations to curate raw data.
• Develop, optimize and maintain existing data models, pipelines, and transformations to support analytics, reporting, and AI use cases such as but not limited to curating, transforming, annotating and modeling data.

• Owning CI/CD pipeline stability across MFT and MFT Enterprise
• Building and baselining the Kubernetes-based on-premise deployment environment for MFT Enterprise
• Reducing single-resource dependency on infrastructure through documentation, cross-training, and automation