Python developer állás, munka

• End-to-end AI solution ownership
• Partnership with product managers and business stakeholders for problem translation
• Independent planning and execution of research and experimentation
• Design of AI solutions with system-level perspective

• Maintenance of the Tulip Operating System including building, packaging, and support
• Configuration and maintenance of routing, networking, and device configurations
• Maintenance and addition of bootloader, kernel patches, and device trees on Tulip hardware
• Porting the Tulip OS to other hardware targets

• Client-facing communication
• Participation in pre-sales discussions
• Collaboration with sales and customer success teams
• Communication with clients' technical teams
• Design and configuration of data labeling and processing pipelines
• Combination of multiple data labeling components into automated workflows

• Active involvement in the software design and test of reference application processor firmware
• Low level software development, test and debug on various platforms including software models, development boards and shipping products

• 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

• 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

• AI-alapú alkalmazások és szolgáltatások fejlesztése Pythonban
• Nagy nyelvi modellek (LLM) integrálása vállalati rendszerekbe
• API-k és újrafelhasználható komponensek építése az AI-megoldásokhoz
• RAG-megoldások és AI-agentek fejlesztése saját infrastruktúrán

• Design and develop the next generation of the Proactive DLP engine in Rust
• Migrate existing C++, C# and Python components to Rust and maintain them until replacement
• Build and orchestrate AI agents that generate, test and refactor code and design validation loops

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

• 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

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

• 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

• Lead business analysis for complex, cross-functional initiatives, from discovery to delivery and post-implementation evaluation.
• Act as a domain expert for customer risk, fraud prevention, and underwriting, and proactively identify opportunities for optimization and innovation.

• 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