• Experience building agentic harnesses from scratch, including capabilities such as tool use, multi-step chaining, reasoning, streaming, skills, multimodal integration, RAG, sandboxing, and state management. • Experience designing, building, and operating production APIs and services, including RESTful APIs, streaming APIs, asynchronous workflows, service boundaries, versioning, authentication/authorization, error handling, and backward compatibility. • Strong judgment around when to use synchronous APIs, event-driven architectures, queues, background jobs, or streaming protocols based on latency, reliability, scalability, and user experience requirements. • Demonstrated ownership of production systems, including process improvements, roadmap contributions, defect resolution, uptime and availability monitoring, and operational reliability. • Strong backend engineering fundamentals, with comfort working across service design, APIs, batch workflows, orchestration, observability, and production support. • Familiarity with evaluation techniques such as creating and maintaining evaluation datasets, running offline regression evals, monitoring online production performance, rubric-based scoring, self-verification, human-in-the-loop review, AI-as-judge methods, and quality/reliability analysis at scale. • Clear understanding of foundational machine learning and statistical concepts, including sampling, statistical significance, overfitting/underfitting, precision and recall, and quality tradeoff analysis. • Experience building and orchestrating large-scale batch workflows that use foundation models, including LLMs and VLMs, as well as pretrained open-source models and deep learning models. • Ability to design systems that safely and reliably automate meaningful enterprise workflows using non-deterministic AI components. • Strong judgment around reliability, failure handling, observability, human review, and operational safety. • Deep understanding of how to make systematic tradeoffs between quality, reliability, latency, cost, explainability, and user experience in complex AI systems. • Ability to design pragmatic architectures that balance innovation with production readiness. • Comfort working in environments where model behavior is non-deterministic and system design must account for uncertainty, evaluation, monitoring, and graceful failure. • Experience with both lexical and embedding-based search methods. • Ability to reason about relevance, ranking, latency, recall, precision, indexing strategy, and retrieval performance. • Experience working with foundation models and open-source LLMs beyond simple API calls. • Familiarity with lower-level model behaviors and controls, including temperature, top-p sampling, logprobs, confidence scoring, prompting strategies, and model selection tradeoffs. • Comfort and willingness to build front-end experiences using JavaScript/TypeScript and frameworks such as React. • While the role is primarily backend and AI systems focused, the ability to contribute to user-facing product experiences is important. • Willingness to work across the stack and own the end-to-end product experience is essential. • Familiarity with tools such as FFmpeg, OpenCV, or similar media-processing libraries is a plus.

• Define and architect enterprise Splunk architecture supporting high availability, scalability, resilience, disaster recovery, and long-term growth • Design and oversee distributed Splunk environments including: Indexer Clusters, Search Head Clusters, Deployment Servers, Cluster Managers, Heavy Forwarders, Universal Forwarders, License Management, Splunk Cloud and Hybrid architectures • Develop technical roadmaps, modernization strategies, migration plans, and platform lifecycle recommendations • Evaluate current platform capabilities and recommend architectural improvements supporting SOC operations and enterprise cybersecurity initiatives • Serve as the technical lead for all Splunk engineering activities • Provide mentorship and technical direction to Splunk Engineers, Security Engineers, Detection Engineers, and SOC Analysts • Review architecture, engineering designs, implementation plans, dashboards, searches, integrations, and custom applications • Establish engineering standards and best practices across index naming, Sourcetypes, CIM compliance, field extractions, knowledge objects, configuration management, change control, application lifecycle management, role-based access control • Architect scalable and resilient Splunk infrastructure supporting enterprise data volumes • Lead platform optimization including Search performance, Index performance, Storage management, Retention policies, License utilization, Data lifecycle management, Search concurrency, Data Model Acceleration • Guide upgrades, patching, backup, disaster recovery, and high availability planning • Lead troubleshooting of complex ingestion, parsing, indexing, search, and performance issues • Define enterprise data onboarding strategies across security, infrastructure, cloud, identity, endpoint, network, and application data sources • Oversee parsing, routing, index design, field extraction, source normalization, CIM implementation, retention policies, data quality • Lead integrations with firewalls, IDS/ IPS, EDR, NDR, Identity providers, Cloud platforms, Vulnerability management, Ticketing systems, SIEM and SOAR technologies, Threat Intelligence platforms • Architect Splunk capabilities supporting Threat Detection, Threat Hunting, Incident Response, Security Monitoring, Risk-Based Alerting, Compliance Reporting • Guide development of Correlation Searches, Dashboards, Reports, Data Models, Detection Content, Enterprise Security Content • Improve detection fidelity while reducing false positives • Ensure Splunk capabilities support rapid investigation, evidence collection, event reconstruction, and executive reporting • Architect automation strategies using Splunk SOAR • Design and oversee playbooks for Incident enrichment, IOC validation, Threat intelligence lookups, Malware analysis, Case management, Automated containment, Notification workflows • Integrate SOAR with enterprise security technologies using Python, REST APIs, and supported applications • Establish automation governance and reusable orchestration standards • Establish engineering governance for Configuration Management, App Lifecycle, Knowledge Object Management, Detection Content, Source Onboarding, Dashboard Standards • Review deployment packages and engineering deliverables prior to production release • Ensure engineering activities comply with change management, testing, documentation, rollback, and operational readiness requirements • Serve as the primary technical advisor for Splunk architecture and platform strategy • Communicate technical risks, roadmap priorities, and architectural recommendations to executive leadership and program stakeholders • Translate business and mission requirements into scalable technical solutions • Coordinate activities with infrastructure, cloud, networking, identity, cybersecurity, and vendor teams • Develop and maintain Architecture diagrams, Engineering standards, Technical designs, Runbooks, Standard Operating Procedures, Disaster Recovery documentation, Knowledge Base articles, Technical decision records • Evaluate emerging Splunk capabilities and recommend improvements to maximize platform value • Foster engineering excellence through mentorship, standards, and continuous process improvement

Role Description El futuro se construye con talento, pasión e innovación. En Paradigma creemos que la Inteligencia Artificial Generativa está transformando la forma en la que las organizaciones diseñan productos, automatizan procesos y crean nuevas experiencias para sus clientes. Buscamos un Platform Architect especializado en IA Generativa que quiera liderar la definición de una plataforma tecnológica moderna, escalable y reutilizable sobre AWS, ayudando a construir los cimientos sobre los que se desarrollarán las soluciones de IA del futuro. No buscamos únicamente a alguien que diseñe arquitecturas. Buscamos a una persona capaz de definir estándares, crear componentes reutilizables y acompañar técnicamente a los equipos para acelerar el delivery con calidad y coherencia tecnológica. Misión: - Ser el responsable de diseñar y evolucionar la plataforma de IA Generativa de Paradigma. - Definir arquitecturas de referencia, componentes reutilizables y buenas prácticas que permitan desarrollar soluciones robustas, escalables y seguras sobre AWS. - Trabajar junto a equipos de Arquitectura, Delivery y Tecnología para construir una plataforma que permita acelerar el desarrollo de asistentes inteligentes, soluciones RAG, agentes y sistemas multiagente. Responsabilidades dentro de Paradigma Digital: - Definición de Arquitecturas de Referencia - Diseñar arquitecturas cloud para soluciones de IA Generativa. - Definir patrones reutilizables para asistentes inteligentes, RAG y sistemas multiagente. - Estandarizar la arquitectura utilizada en los diferentes proyectos de IA. - Desarrollo de Plataforma - Diseñar y construir building blocks reutilizables ("LEGO tecnológico") que puedan utilizar todos los equipos de Delivery. - Crear templates de infraestructura mediante Infrastructure as Code. - Definir componentes comunes para despliegues, autenticación, observabilidad y monitorización. - Evolución Tecnológica - Evaluar continuamente nuevos servicios cloud relacionados con IA Generativa. - Analizar nuevas capacidades de Amazon Bedrock y del ecosistema AWS. - Proponer mejoras continuas para la plataforma. - Gobierno Técnico - Definir estándares de seguridad, mantenibilidad y escalabilidad. - Garantizar la coherencia tecnológica entre diferentes proyectos. - Documentar patrones arquitectónicos y mejores prácticas. - Soporte a Delivery - Acompañar técnicamente a los equipos durante el diseño de soluciones. - Ayudar en la toma de decisiones arquitectónicas relevantes. - Resolver dudas sobre infraestructura, despliegue e integración con sistemas corporativos. Qualifications - Experiencia diseñando arquitecturas cloud sobre AWS. - Experiencia trabajando con Infrastructure as Code. - Experiencia desarrollando soluciones cloud-native. - Conocimiento de servicios serverless. - Experiencia participando en proyectos reales de IA Generativa. - Capacidad para definir arquitecturas reutilizables. - Nivel alto de autonomía técnica. Requirements - Cloud - AWS - Amazon Bedrock - AWS CDK (preferiblemente Python) - CloudFormation - API Gateway - Lambda - IAM - EventBridge - Step Functions - Infrastructure as Code - AWS CDK - CloudFormation - Terraform (muy valorable) - IA Generativa - Large Language Models (LLMs) - Amazon Bedrock - Retrieval-Augmented Generation (RAG) - Prompt Engineering - Model Evaluation - Agentes inteligentes - Sistemas Multiagente - Tool Calling - Model Context Protocol (MCP) - Datos - Bases de datos vectoriales - Embeddings - Chunking - Knowledge Bases - Arquitectura - Arquitecturas Cloud Native - Seguridad Cloud - Escalabilidad - Observabilidad - Monitorización - Integración con APIs y sistemas corporativos Benefits - Flexibilidad real: Conciliación entre vida personal y laboral con horarios adaptables. - Innovación y modelo propio: Proyectos retadores donde podrás trabajar y evolucionar Polaris, nuestro modelo ágil adaptable, basado en nuestra experiencia. - Comunidad experta: Te unirás a una comunidad de ágil activa, donde ampliamos conocimiento de forma colaborativa con el objetivo de ser un referente en el sector. - Capacitación real: Acceso a un plan de formación anual que incluye un Programa de Certificaciones y formaciones oficiales. - Acompañamiento desde el inicio: Contarás con un Buddy desde tu incorporación, que te ayudará a crecer, apoyarte y sentirte parte de Paradigma. - El dominio técnico son parte de nuestra identidad. - Beneficios sociales: Seguro médico 100% cubierto.

Role Description We are seeking an experienced STIBO STEP MDM Architect with 10+ years of experience in Master Data Management (MDM) and strong expertise in Stibo STEP MDM/PIM implementations. The ideal candidate will lead the design, architecture, and delivery of scalable MDM solutions, collaborate with business stakeholders, and drive end-to-end implementation using industry best practices. Qualifications - 10+ years of MDM experience with hands-on expertise in Stibo STEP MDM/PIM - Experience leading multiple end-to-end Stibo STEP implementations - Strong knowledge of Product, Customer, and Supplier data models - Expertise in solution architecture, configuration, workflows, business rules, and data migration - Experience with integrations, data governance, and technical design - Excellent customer-facing, communication, leadership, and problem-solving skills Company Description