Python programmer állás, munka

• 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

• Tackling complex technical challenges focused on scalability, reliability, and speed
• Maintaining a high-performance infrastructure that processes millions of events with low latency
• Collaborating with Data Scientists and Engineers to design and implement cutting-edge feature topologies

• 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 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

• Lead AI-assisted software engineering activities for Meridian by designing repeatable SDLC patterns
• Anchor the engineering approach for OpenStack-derived services, repository-scale understanding, and sovereign software handover readiness

• Service-level analysis of modular cloud software
• Mapping APIs, configuration flows, service dependencies, database usage, and operational assumptions of OpenStack-style services and surrounding components

• On-premise IT infrastruktúra üzemeltetése, támogatása és folyamatos fejlesztése
• Linux alapú rendszerek adminisztrációja
• Kubernetes klaszterek adminisztrációja
• Middleware komponensek adminisztrációja
• Automatizációs feladatok megvalósítása és karbantartása

• 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

Development and enhancement of the MSCI Index Development Platform and the IndexMetrics Real-Time application using modern software engineering practices, Python, Azure, and AI-assisted development tools...

• 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

• Active partnership with sales and project managers to estimate, design, develop and deliver solutions
• Continuous communication with domain experts and product owners to understand core business needs and goals to synthesize requirement and quality standards

• 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 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

• 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

• Building and maintaining dataset ingestion, cleaning, and versioning pipelines
• Designing and operating labeling workflows including quality control and inter-annotator agreement tracking
• Owning dataset testing including coverage analysis, class balance, leakage detection, and dataset documentation

• 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

• Architect cloud-native AI applications integrating LLMs vector search and real-time data
• Design end-to-end web solutions from frontend to backend
• Implement RAG pipelines model serving and AI agents in production
• Lead cross-functional teams (Full-stack AI Data)

• Building AI-assisted development workflows for code understanding, documentation, build triage, and handover preparation
• Implementing workflow utilities, prompt packs, repository analysis scripts, documentation helpers, and integration adapters