Python programmer állások XVI. kerületben (ai és automatizáció)

• Build and maintain dataset ingestion, cleaning, and versioning pipelines
• Design and operate labeling workflows including quality control and inter-annotator agreement tracking
• Own dataset testing including coverage analysis, class balance, leakage detection, and dataset documentation

• 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

• 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

• Implementation and maintenance of large-scale data pipelines and the Azure/Databricks data platform
• Building robust, scalable pipelines using Azure Databricks (PySpark / SQL)
• Design and enforcement of data architecture standards including Delta Lake / Lakehouse architecture and data modeling

• 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

• 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

• 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

• 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

• Shaping engineering strategy with broad organizational impact
• Driving platform evolution by identifying cross-cutting pain points and leading the design of secure, scalable, reusable solutions built on AWS and modern serverless and microservice patterns

• 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

• 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

• Development and maintenance of the data platform’s infrastructure using Terraform
• Implementation and management of CI/CD pipelines for automation of data and AI processes and pipelines
• Automation of provisioning, configuration, and monitoring of resources in AWS

• 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