Python programmer állások Budapesten (ai és automatizáció)

• CI/CD pipeline-ok kialakítása, fejlesztése és karbantartása
• Deployment és release folyamatok automatizálása
• Cloud infrastruktúrák üzemeltetése és optimalizálása
• Konténerizált környezetek kezelése (Docker, Kubernetes/OpenShift)
• Monitoring, logging és alerting rendszerek kialakítása és üzemeltetése

• Work with an agile mindset and according to agile methodology
• Collaboration within the team, with other teams in the Domain and Deutsche Telekom, and with software engineers using the cloud platform
• Approach work with a DevOps and continuous improvement mindset

• Building, configuring and maintaining bare metal hosts and Kubernetes clusters to run GPU/AI workloads
• Designing and operating NVIDIA AI related software stack (Slurm, Run AI)
• Providing customized application support for AI related workloads

• 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

• Development of new applications and services expanding the cloud platform ecosystem
• Coding, testing and frequent deployment embracing modern development practices
• Automation with common automation frameworks to eliminate manual work

• 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

• Cooperation with software teams to establish and implement quality processes at every stage of the development cycle
• Development of automated test cases for firmware and Linux-based systems including bootloaders, kernel, device drivers and applications

• Design of reliable cloud architectures
• Partnership with customers, SRE teams, and internal stakeholders
• Championing automation
• Building reusable Infrastructure as Code templates and utility tools
• Design of secure cloud networks
• Driving customer success
• Operating platforms at scale

• Collaboration within the team and with other teams in the Domain and Deutsche Telekom using agile methodology
• Definition and ownership of target-state GCP network architecture including hub-and-spoke/shared VPC, segmentation, routing domains, ingress/egress patterns

• 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

• Design and implement automation of data platform
• Develop and maintain data platform infrastructure using Terraform
• Implement and manage CI/CD pipelines for automation of data and AI processes and pipelines
• Automate provisioning, configuration, and monitoring of resources in AWS

• Definition and communication of architectural vision for the Cloud Management Portal aligned with product strategy and customer outcomes
• Pragmatic architectural decisions balancing modern practices with delivery speed and customer value

• Experience in different data science topics
• Independent delivery of data science projects with e2e product-oriented mindset
• Direct engagement with clients to understand business objectives and translate into technical requirements
• Leading pre-sales technical discussions

• Design, develop, and implement complex and scalable data solutions on the AWS cloud platform
• Design, build, and optimize data pipelines to ensure efficiency and reliability
• Write clean, efficient, and maintainable SQL and Python code
• Develop and maintain shell scripts to support automation

• Design, build, and ship production retrieval systems
• Own vector store selection and operation
• Build and operate indexing and refresh pipelines
• Implement semantic and hybrid retrieval
• Own retrieval evaluation as a first-class system
• Make and defend the core tradeoffs of the domain

• Design of evaluation framework for scalable LLM-as-a-judge pipelines
• Development of rubrics and datasets including comprehensive grading rubrics and high-quality golden datasets
• Optimization of judge prompts for automated scoring alignment

Architect, build, and operate scalable inference services, APIs, and backend components for model-driven and LLM-powered product features...

• Design and build knowledge tracing and longitudinal learner models that support mastery and progression features surfaced to customers
• Define what "mastery" and "progression" mean operationally: constructs, model targets, and evaluation criteria aligned to learning outcomes and product requirements

• 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

• 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