Python állások II. kerületben (it programozás, fejlesztés)

• BI alkalmazások fejlesztése (.NET C#, django – python)
• Enterprise Unit belső szervezetei számára eseti és rendszeres riportok készítése
• EDWH (Enterprise Data Warehouse) – a TRI adattárházának fejlesztése, új üzleti tartalmak implementálása
• Power BI riportok, dashboardok készítése
• Adatmigráció

• Fejlesztés és telepítés AI-alapú alkalmazások
• Integráció Large Language Model-ekkel üzleti megoldásokban
• Skálázható backend szolgáltatások építése Python vagy Node.js használatával
• Felhasználóbarát felhasználói felületek fejlesztése React vagy Angular segítségével

Shaping and evolving the architecture of MDO network automation capabilities including reusable automation patterns, interfaces and technical guidelines...

• Design, develop, and implement complex, scalable Lakehouse solutions on the Databricks platform across major cloud environments (Azure, AWS, or GCP)
• Design, build, and optimize modular data pipelines using Delta Lake to ensure data efficiency, reliability, and quality

• Development and implementation of complex generative AI applications from discovery to deployment
• Establishment of best practices framework for deploying cloud architectures supporting AI agents
• Creation of RAG/LLM evaluation pipelines, embedding strategies, and prompt/runtime optimization

• Design and implement modern data architectures and lakehouse solutions
• Provide technical leadership throughout the project lifecycle
• Collaborate with business stakeholders
• Create effort estimations
• Provide technical expertise during the pre-sales phase
• Mentor 3-6 person project teams

• 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

• Managing and migrating ETL pipelines between various source systems
• Validating pipeline build success and output data quality
• Communicating with business stakeholders and SME's regarding their respective data domain
• Analysing and resolving blockers and issues with ETL pipelines and data discrepancies

• 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

• Operational efficiency improvement through process optimization, automation, and adoption of new technologies
• Understanding the company's data warehouse and aligning data flows with reporting requirements
• Development and maintenance of reports and data-driven solutions using Power BI and Google Sheets

• Architecture and delivery of production-grade LLM systems and classical ML solutions
• Design, evaluation, and optimization of RAG pipelines
• Building scalable, production-grade LLM services and agentic workflows alongside traditional ML systems

• Development of the test automation framework
• Development of test cases/steps
• Development of test tools
• Integration of testing tools into the automation framework

• Design and implement enterprise-scale data pipelines and advanced analytics dashboards
• Develop and optimize data ingestion, transformation, and modeling workflows across structured data environments and cloud-based platforms

• Experience in different data science topics including machine learning, LLM, deep learning
• Independent delivery of data science projects with e2e product-oriented mindset
• Direct engagement with clients to understand business objectives and translate into technical requirements

• End-to-end penetration testing engagements across web applications, APIs, cloud environments, binary applications and internal/external infrastructure
• Simulation of real-world attacks to identify security risks

• Design and implement cloud security controls across AWS, Azure, and GCP
• Build security automation using CI/CD pipelines, Infrastructure as Code, and Policy as Code
• Develop automated compliance checks and remediation workflows

• Development and fine-tuning of AI models including LLMs, Agentic AI, and Generative AI systems
• Building and deploying robust AI applications and services on cloud platforms
• Design and implementation of efficient AI pipelines from data preparation to model deployment and monitoring

• Development and operation of data pipelines
• Building data layers in OneDataLake (ODL) Central Data Store Consumer (CDSC)
• Balancing business and IT demands to build modern and efficient pipelines in public cloud environments
• Integration of data preparations into managed processes

• Design, develop, and maintain automated test suites as code using Java
• Create BDD-based automated test scenarios using Cucumber against REST APIs
• Automate API testing for OAuth 2.0 and OpenID Connect (OIDC) authentication and authorization flows using RestAssured or similar tools

• Building AI-assisted development workflows
• Testing AI-assisted development workflows
• Maintaining AI-assisted development workflows
• Implementing workflow utilities
• Implementing prompt packs
• Implementing repository analysis scripts
• Implementing documentation helpers
• Implementing integration adapters