Job Description Summary The Employee Experience & Engagement Leader within GE Vernova’s Learning & Development (L&D) organization plays a critical role in shaping how our global workforce discovers, accesses, and engages with learning. This role blends expertise in employee experience, brand, learning engagement, and digital experience design to create a cohesive, intuitive, and inspiring learning ecosystem that supports GE Vernova’s mission to electrify the world while working toward a more sustainable future. A core responsibility is leading the user experience transformation of our Learning Management System (LMS), ensuring excellence in user experience (UX) and search engine optimization (SEO). All Learning technology strategies will reflect GE Vernova’s identity, be supported by talent development strategy, and enable employees to build the skills needed for the energy transition. Job Description Key Responsibilities: Strengthening the Employee Learning Experience - Design and lead the end‑to‑end employee experience for learning across GE Vernova’s global footprint. - Ensure learning touchpoints—from discovery to completion—are intuitive, inclusive, and aligned with HR and business priorities. - Build a unified learning-branded ecosystems that reflects GE Vernova’s culture, values, and commitment to innovation and sustainability. - Partner with HR Business Partners, Talent Management, and L&D program owners to ensure learning offerings support capability-building across the enterprise. - Drive content “market mapping” across the collective suite of Learning offerings to enable a curated experience that represents the brand. - Lead the development and execution of scalable global digital experience strategies supporting the Digital Transformation Roadmap ensuring content relevance and governance across business units. - Monitor emerging technologies and trends to identify opportunities for innovation that elevate the learner experience. - Engage with industry partners for benchmarking and best practice sharing. Skills Programming - Develop strategies that increase awareness, adoption, and impact of learning programs and platforms. - Partner with the Communications team to highlight learning success stories, capability-building initiatives, and the role of skill development in GE Vernova’s transformation. - Develop a short and long term strategy to reflect and measure skill growth via tool capability and behavior change that resonates across functions, regions, and the employee life cycle. LMS User Interface & Governance - Serve as the strategic owner of the LMS user experience, ensuring the platform is modern, intuitive, and aligned with learner needs. - Lead UI/UX experience integration (of multiple platforms), including navigation improvements, visual design updates, content organization, and accessibility optimization. - Collaborate with Global Solutions, Digital Technology, and external vendors to implement design changes, test new features, and maintain platform stability. - Conduct regular audits of the LMS interface and content structure to identify opportunities for simplification, modernization, and improved discoverability. Continuous Improvement - Continuously identify opportunities to innovate and evolve the employee learning experience by leveraging first-party data, digital tools, and other platforms to deliver personalized, insight-driven experiences. - Develop a data strategy and customer (employee) relationship roadmap to deepen relationships, enable data-driven personalization, and support audience segmentation, targeting, and engagement strategies across the digital learning ecosystem. - Utilize analytics platforms to create engagement KPIs, assess performance, uncover insights, and continuously optimize digital experiences. - Develop governance standards for learning content presentation, naming conventions, and user pathways to ensure consistency across the enterprise. - Use analytics, employee feedback, and HR insights to drive continuous improvement of the learning experience and learning brand that aligns with GE Vernova’s talent strategy, digital transformation goals, and compliance requirements. - Lead any Learning & Development change management efforts, in partnership with the CMO, for new learning technologies, processes, and enterprise-wide initiatives. Cross‑Functional Collaboration - Foster a collaborative, curious, and creative team culture by building strong internal partnerships and promoting a growth-oriented mindset within GE Vernova and the Learning community. - Partner closely with regional Marketing, Learning, Communications, Digital Technology and HR partners to build strong relationships and optimize processes to support collaboration and information sharing; ensure learning experiences are integrated and impactful. - Provide standard work to content creators, SMEs, and HR partners on GE Vernova's learning model and learner‑centric design. - Facilitate listening sessions (genba), focus groups, and feedback loops to ensure employee voice informs strategy and design decisions. Required Qualifications - Bachelor’s degree, or equivalent knowledge or experience from an accredited university or college, (or high school diploma with significant experience) - Minimum of 10 years’ experience in Learning & Development, HR, Employee Experience/Engagement, Marketing, UX or related field - Ability and willingness to travel up to 10% Preferred Location: Untied States. While this is preferred, for the right candidate we remain open to remote applicants elsewhere. Desired Qualifications - Experience with developing go-to-market product solutions - Workday Learn/Workday HCM experience - Experience working with various LXP platforms - Strong understanding of LMS platforms, digital learning ecosystems, integrations and user-centered design principles. - Demonstrated ability to embrace and influence change. - Experience working in a global, matrixed organization; energy or industrial sector experience is a plus. - Ability to translate complex information into clear, engaging, and accessible learning experiences. - Strong collaboration, stakeholder management, and project leadership skills. - Familiarity with analytics tools and methods for measuring engagement and experience quality. - Demonstrated AI literacy What Success Looks Like - A learning ecosystem that is cohesive, intuitive, and aligned with GE Vernova’s strategy. - Increased employee engagement with learning programs and platforms across global teams. - A modernized LMS interface that improves discoverability, reduces friction, and supports capability-building. - Clear, compelling user experiences that demonstrate the value of learning and skill building received from GE Vernova. - A sustainable governance model with KPIs that ensure consistency, quality, and continuous improvement across all learning experiences. Additional Information GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law. GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). Relocation Assistance Provided: No #LI-Remote - This is a remote position Application Deadline: April 27, 2026 For candidates applying to a U.S. based position, the pay range for this position is between $122,400.00 and $203,900.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set. Bonus eligibility: discretionary annual bonus. This posting is expected to remain open for at least seven days after it was posted on April 20, 2026. Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off. GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.

Descripción de la empresa INETUM Somos un Consultora digital, internacional y ágil. En la era de la post-transformación digital, nos esforzamos para que cada uno de nuestros 28.000 profesionales pueda renovarse continuamente. Cada uno de ellos puede diseñar su itinerario profesional de acuerdo a sus preferencias, emprender junto a sus clientes para construir en la práctica un mundo más positivo, innovar en cada uno de los 27 países y conciliar su carrera profesional con su bienestar personal. Nuestros 28.000 atletas digitales están orgullosos de haberse certificado Top Employer Europe 2024 Descripción del empleo Como Data Scientist / Machine Learning Engineer, serás una parte fundamental de nuestro equipo, desempeñando un papel fundamental en el aprovechamiento de conocimientos basados en datos para mejorar nuestros productos y servicios, así como en el desarrollo e implementación de soluciones de ML y aprendizaje profundo de vanguardia en una variedad de casos de uso. Tus responsabilidades abarcarán una amplia gama de casos de uso, desde la visión por ordenador para la detección y reconocimiento de objetos, el mantenimiento predictivo, la detección de anomalías, hasta proyectos pioneros de IA generativa y optimización del diseño. Trabajaréis estrechamente con equipos multifuncionales para diseñar, desarrollar y desplegar modelos de IA que mejoren nuestra eficiencia e innovación. Requisitos ✅ Responsabilidades clave - Diseñar, desarrollar y desplegar modelos de IA, incluyendo pero no limitándose a visión por ordenador e IA generativa, para resolver problemas complejos en la industria automovilística. - Colaborar con equipos multifuncionales para comprender los retos empresariales e identificar oportunidades para aprovechar las tecnologías de aprendizaje automático. - Conocimiento de Azure ML Studio - Utilizar modelos de IA generativa para innovar en diseño, investigación y desarrollo y procesos de fabricación. - Optimizar los modelos de aprendizaje automático existentes para rendimiento y escalabilidad, aprovechando las arquitecturas avanzadas de GPU CUDA y los marcos de computación paralela para un cálculo eficiente. - Diseñar e implementar pipelines de aprendizaje automático para preprocesar datos, entrenar modelos y evaluar métricas de rendimiento. - Utiliza el lenguaje de programación Python y frameworks de aprendizaje profundo como PyTorch y TensorFlow para implementar soluciones de aprendizaje automático. - Aplicar técnicas estadísticas y algoritmos de aprendizaje automático para resolver problemas complejos en el sector automovilístico. - Mantente al día de los últimos avances en ciencia de datos, aprendizaje automático, aprendizaje profundo e inteligencia artificial, y aplica este conocimiento para impulsar la innovación continua dentro de la empresa. - Desarrollar y mantener documentación para metodologías de análisis de datos, desarrollo de modelos y procesos de despliegue, garantizando la reproducibilidad y el cumplimiento de los estándares del sector. Requerimientos: - Doctorado o máster en Informática, Inteligencia Artificial, Aprendizaje Automático o una carrera relacionada. - Experiencia probada en aprendizaje automático, aprendizaje profundo e inteligencia artificial. - Conocimiento de Azure ML Studio - Excelente comprensión del lenguaje de programación Python, CUDA y arquitecturas de GPU para computación paralela y optimización de modelos. - Sólido conocimiento de arquitecturas y técnicas de aprendizaje automático, con especial atención a la visión por ordenador y la IA generativa. Tratamiento y estudio de imagenes - Competencia en frameworks de deep learning como PyTorch y TensorFlow. - Experiencia con computación paralela y optimización para entornos de computación de alto rendimiento. - Capacidad para trabajar de forma colaborativa en equipo y comunicar conceptos complejos de forma clara a los actores no técnicos. - Pensamiento innovador con pasión por abordar problemas complejos y superar los límites tecnológicos. - Sólida base en arquitecturas, técnicas y frameworks de aprendizaje automático, con especial énfasis en aplicaciones en visión por ordenador e IA generativa. - Excelentes habilidades analíticas, con la capacidad de interpretar datos complejos y traducir hallazgos en perspectivas estratégicas y soluciones prácticas. - Sólidas habilidades de comunicación, capaces de transmitir conceptos técnicos a partes interesadas no técnicas. - Mínimo de 5 años de experiencia en un puesto de ingeniería de aprendizaje automático, preferiblemente en la industria automotriz o en un sector relacionado. - Experiencia demostrada en el desarrollo y despliegue de modelos de aprendizaje automático para aplicaciones reales. - Automotivado con pasión por el aprendizaje continuo y el desarrollo profesional. - Experiencia trabajando en Agile y en un proveedor de nube (como Azure) sería un plus Información adicional ¿Qué podemos ofrecerte: - Formarás parte de un gran equipo de profesionales con inquietud y motivación por el desarrollo y la programación. - Trabajarás a jornada completa con un horario flexible bajo un modelo de trabajo 100% remoto. - 22 días de vacaciones + 2 de asuntos propios. - Formación por parte de la empresa para que puedas seguir desarrollándote y promocionar dentro del plan de carrera que existe para ti. - Podrás asistir a eventos y conferencias relevantes del sector. - Contrato indefinido. - Estabilidad y buen clima laboral. - Salario Competitivo. - Acceso a ventajas del grupo de empresa - Retribución flexible y más beneficios

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems • Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support • Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters • Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems • Build and maintain CI/CD workflows for ML services, model artifacts, and platform components • Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment • Improve observability, reliability, security, and maintainability across ML infrastructure and services • Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads • Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments • Support compliance-driven deployment practices and secure software supply chain requirements in defense environments • Get hands-on with customers at the most forward-leaning places in the Department of War

• Training Machine Learning models • Processing and understanding large datasets • Optimizing ML algorithms for performance and scalability • Integrating ML models into existing software applications • Collaborating with data scientists, business analysts, and software engineers • Staying up to date with the latest ML technologies and best practices • Following software engineering best practices