Python programozó diákmunka állások IV. kerületben (it programozás, fejlesztés)

• 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

Support Meridian technical due diligence and transition readiness by using AI-assisted software engineering methods to understand OpenStack-derived cloud services...

• Strengthening Meridian software handover readiness by combining AI-assisted software engineering with code quality, security validation, dependency analysis, and build evidence generation for complex cloud platform repositories
• Trustworthy validation of large codebases

• 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

• Training and fine-tuning models against curated datasets
• Running systematic hyperparameter and architecture experiments
• Building and maintaining evaluation suites including regression tests for model behavior and benchmark tracking

• Industrialize AI-assisted engineering workflows for Meridian
• Package repeatable patterns supporting codebase intake, dependency extraction, build triage, documentation generation, and technical evidence creation across cloud software work packages

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...

• Design and build agentic systems that plan, call tools and automate real business workflows
• Develop LLM-powered applications including RAG pipelines: chunking, embedding, retrieval strategies, evaluation and iteration

• 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

• Planning, coordination, and delivery of signal detection and product safety scientist activities
• Support of mentoring of more junior colleagues
• Development of processes, tools, and systems
• Serving as lead on smaller FSP-type projects

• 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

• Design and implement algorithms to process and analyze sensor data from sources like LiDAR, images, and satellite imagery
• Create algorithms to transform sensor data into actionable insights, such as reconstructing 3D models, segmenting spatial data, or detecting environmental features

• 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

Ownership of detection capabilities end to-end from problem framing and data through experimentation to production models and telemetry...

• 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

• The CloudOps Engineer is responsible for availability for cloud platforms
• Respond, troubleshoot and resolve production alarms
• Communicate and troubleshoot operational issues supporting a complex environment
• Respond to cloud service outages and manage incident tickets

• 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

• Környezet kialakítása és fejlesztése (DEV)
• A data streaming platform (Confluent Platform / Apache Kafka) komponenseinek telepítése, konfigurálása és karbantartása a fejlesztői környezetben.

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

• Building AI-assisted development workflows for code understanding, documentation, build triage, and handover preparation
• Implementing and maintaining AI-assisted engineering utilities for repository indexing, code summarization, dependency extraction, log parsing, and documentation generation