Python developer állás, munka

• Co-design and implement QA workflows using statistical diagnostics, validation rules, and anomaly detection techniques
• Apply AI/machine learning tools to identify inconsistencies, missing values, or suspicious patterns in large-scale asset datasets

• Design, develop, and maintain C-based applications in Linux/UNIX environments
• Work with system-level interfaces (POSIX APIs, processes, file systems, sockets)
• Develop and enhance Kerberos authentication systems and related tooling

• Shaping AWS-based cloud architectures and technical decisions for SaaS, PaaS, web application, and enterprise cloud environments
• Design and implementation of Infrastructure as Code solutions using Terraform, AWS CloudFormation, or Pulumi

• Development of the Tulip OS
• Maintenance and support of bootloader, kernel, and userspace for Tulip hardware
• Identification of key features and product performance alongside product management and customers
• Maintenance of the Tulip Operating System including building, packaging, and support

• Building and operating scalable data ingestion and CDC capabilities on Azure-based Lakehouse platform
• Developing pipelines in Azure Data Factory and Databricks
• Maturing engineering approach through Python projects and reusable frameworks

• Design, develop, and implement complex and scalable data solutions on the AWS cloud platform
• Design, build, and optimize data pipelines to ensure efficiency and reliability
• Write clean, efficient, and maintainable SQL and Python code
• Develop and maintain shell scripts to support automation

• 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

• Leading AI-assisted software engineering activities for Meridian
• Designing repeatable SDLC patterns for analysis, validation, documentation, and evolution of complex cloud software stacks

• Build and operate multi-cluster containerized environments across cloud platforms
• Create end-to-end CI/CD pipelines for cloud-native applications and machine learning workloads
• Lead cost-optimization and FinOps initiatives for GPU, storage and data-egress spend

• 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 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

• 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

• 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

• Monitoring configuration, maintenance and usage of environments with virtual machines and cloud based infrastructures (Kubernetes)
• Cooperation with platform engineers, software architects, delivery teams and operational units

• Unterstützung des Teams bei der Automatisierung und dem Testen von Cisco SD-WAN basierten Netzwerktechnologien
• Entwicklung und Pflege automatisierter Testfälle mit Python und Robot Framework
• Nutzung von Jira für Aufgabenverfolgung
• Kommunikation über MS Teams

• Design and implement cloud security controls across AWS, Azure, and/or GCP
• Deploy and tune Cloud Security Posture Management and Cloud-Native Application Protection Platform tooling
• Build security into Infrastructure-as-Code and CI/CD pipelines

• Ownership of backend services throughout their lifecycle including design, implementation, testing, deployment, operation, and continuous improvement
• Translation of product and business requirements into maintainable technical designs with clear communication of risks and trade-offs

• Komplex AI megoldások fejlesztése ügyfelek számára
• RAG alapú megoldások fejlesztése Python/LangChain vagy Java/Spring AI technológiákkal
• Open source LLM-ek integrálása ügyfélrendszerekbe cloud és on-premise környezetben Kubernetes használatával

• 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

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