Python programmer állás, munka

• Create and implement test strategies and concepts for all test levels in embedded, cloud, mobile and web applications
• Improve our development and testing processes, methods and tools, and support the development teams
• Plan, set up and maintain test environments and automation frameworks

Analyze AI computing systems end-to-end: Understand how training and inference workloads run on modern data-center architectures and identify the fundamental bottlenecks in compute, memory, interconnects and energy efficiency....

• Research and identify suitable prediction approaches, including kinematic and dynamic models, Kalman filters, regression techniques, data-driven methods, and hybrid approaches
• Implement prototypes in Python and integrate selected methods into C++ applications where appropriate

• An opportunity to build the foundation for next-generation products
• The option to write a master thesis following an internship or working student position
• Hands-on work on intrinsic, extrinsic, and multi-camera calibration for ZEISS use cases

• Lead design, development, implementation, validation, and continuous improvement of automation systems enhancing Inventory Control processes
• Drive automation projects from concept through deployment ensuring delivery on time, within scope, and aligned with business objectives

• Technical leadership and mentoring of data engineers
• Technical oversight for Databricks solutions
• Design and implementation of scalable ETL/ELT pipelines using Databricks, Python/PySpark, SQL, and Delta Lake

• An opportunity to build the foundation for next-generation products
• The possibility to learn and implement cutting edge 3D computer vision methods
• Mentorship by experienced PhD-level experts
• The option to write a master thesis following the internship

• Adatforrások integrálása a vállalat új Data Lakehouse platformján
• Adatbetöltési folyamatok fejlesztése a vállalat új Data Lakehouse platformján
• Skálázható adatfeldolgozó folyamatok fejlesztése
• Adattranszformációk fejlesztése
• Adatfeldolgozó folyamatok optimalizálása

• Building scalable testing frameworks
• Designing comprehensive test suites covering unit, integration, and acceptance testing
• Collaborating with engineers and data scientists to embed quality practices throughout the development lifecycle

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

• Develop an efficient and reproducible workflow for application-oriented machine learning solutions.
• Be involved in sample preparation and data acquisition in the laboratory for full data ownership

• 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

• 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

• Build and maintain scalable, reliable, and secure environment on AWS using Infrastructure as Code tools
• Oversee the deployment, configuration and management of clusters
• CI/CD pipeline development with designing and managing CI/CD pipelines, Infrastructure as Code, and GitOps toolset and skillset

• Designing, developing, and evaluating machine learning models on complex telecom and retail data
• Transforming high-volume datasets into production-ready solutions such as churn prediction models and point-of-sale fraud models

• Build and maintain scalable cloud infrastructures using modern services and best practices
• Proactive detection, analysis, and fixing of vulnerabilities and performance bottlenecks in cloud environments

Advise business units and senior stakeholders on AI opportunities, strategic priorities, target operating models, and the practical implications of emerging AI technologies...

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

• 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
• Develop intelligent document understanding for PDFs, Word, Excel, presentations

• 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 AI solutions for intelligent document understanding