• Colaborar en un proyecto del sector industrial, buscando la optimización y automatización de los procesos. • Crear modelos de inteligencia artificial. • Realizar análisis de datos, experimentaciones, evaluaciones y montar sistemas de monitorización de modelos de inteligencia artificial.

• Architect, deploy, and lifecycle-manage our enterprise Hyper-V virtualization environments while seamlessly extending Azure management and services to them via Azure Arc. • Implement and enforce Azure Policy, Microsoft Defender for Cloud, and centralized RBAC across all Arc-enabled servers and Hyper-V clusters. • Engineer highly available Hyper-V failover clusters, optimizing storage (e.g., S2D, CSVs) and software-defined networking for peak performance. • Leverage Infrastructure as Code (IaC) and automation tools (PowerShell, ARM templates, Bicep) to streamline the provisioning of on-premises VMs and their onboarding into Azure Arc. • Design and maintain unified monitoring strategies using Azure Monitor and Log Analytics to ensure comprehensive visibility across the entire hybrid boundary.

• Must be capable of performing analysis of system operations • Must be capable of system decomposition via OMG frameworks (UML, DoDAF 2.0/UPDM, SysML, UAF) • Must be able to model systems via different perspectives (e.g. capability, operational, system, functional) and at various levels of abstraction • Must be familiar with UML modeling tools (Cameo Enterprise Architect preferred) • Must be able to meet with stakeholders in a variety of system domains and analyze requirement, process, system, and/or capability • Must be capable of extending UML-based models to other tools or modeling environments via existing frameworks/API’s • Must be familiar with executable models (fUML specification preferred) • Must have the ability to generate/produce data and reports • Interact and professionally exhibit customer service skills and be able to present a positive approach with customers and peers • Other duties and responsibilities as required and assigned

• Develop, maintain, and evolve data pipelines using Apache Airflow (MWAA); • Build data ingestion, transformation, and provisioning processes (ETL/ELT); • Develop solutions using Python, Spark, and AWS services; • Implement and support data architectures in Data Lake, Lakehouse, and Data Warehouse environments; • Perform data modeling to meet analytical and business needs; • Ensure data quality, governance, and observability of processed data; • Monitor and optimize the performance of data pipelines and processes; • Collaborate with business, analytics, and technology teams to evolve the data platform; • Support the definition of best practices, standards, and continuous improvements across the data ecosystem.