Python állások III. kerületben (ai és automatizáció)

• Resource account igénylése és összekötése namespacekkel
• Resource accountba kerülő komponensek megvalósítása RDS, APIk, S3, MSK, cognito, SES, private link endpoint és endpoint service nonprod és prod környezeteken
• RICE API kapcsolatok kialakítása

• Design process of a SoC by integrating the latest Arm IP products
• Development of RTL code using System Verilog
• Running EDA tools to check the design and analyze results
• Using AI-assisted engineering tools to support development and debug
• Identifying and fixing bugs

• Design and build artificial intelligence and machine learning solutions for enterprise clients
• Operationalize AI and machine learning solutions combining custom models with cloud and third-party AI services
• Assess client needs and data for AI/ML solutions

• Design and build learner models including knowledge tracing and longitudinal approaches
• Shape the data foundation for learner modeling including defining relevant signals and building datasets
• Translate mastery and progression definitions into model targets and evaluation criteria

• Ownership of data models and storage architecture behind AI systems across relational, graph, and retrieval-oriented data
• Building and operating APIs, backend services, and pipelines to keep data current and accessible

• Labeling of image data for training of object recognition
• Programming of data acquisition pipelines in Python or C++
• Frontend development of a browser client in Three.js
• Design of 3D props and environments in Blender and Omniverse
• Linux server administration, particularly DGX Spark and Jetson

• 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

• Writing clean, maintainable Python code and automation scripts
• Building CI/CD pipelines and deployment automation for AI systems
• Creating infrastructure as code and configuration management solutions
• Developing APIs, microservices, and event-driven architectures

• Nemzetközi csapat tagjaként hosszú távú tesztelési feladatok különböző projektekben
• Új automatizálási keretrendszer fejlesztése low-code tesztautomatizáláshoz
• A keretrendszer karbantartása, gondozása és folyamatos továbbfejlesztése
• Innováció és új technológiák aktív bevonása és alkalmazása

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

• Fejlesztés és megvalósítás komplex generatív AI alkalmazásokban a felfedezéstől a bevezetésig
• Legjobb gyakorlatok keretrendszerének kialakítása felhőarchitektúrák telepítéséhez AI ügynökök támogatására, költség, teljesítmény és biztonság fókuszával LLM munkaterhelésekhez és vektor kereséshez

• 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

• 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

• 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