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AI and BI Internships en Valencia, España - Jobeax

AI and BI Internships en Valencia, España is listed on Jobeax. Browse 80,000+ vacancies available.

What you'll get

Specific tailored training paths in DATA SCIENCE, AWS, GCP, AZURE, IBM, PYTHON, PYSPARK, and SAS.

For the Master's in Data Science: Understanding how a real work environment operates, where the resolution of projects related to Business Intelligence is addressed.
Gaining an overall view of the common terms and concepts used in the Business Intelligence context.
Participating in the development of solutions across any of the components (online, batch, and/or analytical layer). Collaborating within a multidisciplinary team, using methods and tools focused on this type of project. Contributing each student's specific knowledge to the application of algorithms and techniques related to Machine Learning use cases.

Profile we are looking for

We are looking for students pursuing a Master's degree in AI, with a high level of English (C1), motivated to grow in a dynamic and technological environment.

General Objectives:

Understand and experiment with the current possibilities of Generative Artificial Intelligence in text, image, audio, or code tasks.
Participate in the critical evaluation of the ethical and business impact of solutions based on GenAI.
Learn best practices for the responsible, secure, and efficient integration of generative models into real products.

GenAI Modeling and Application

Evaluation and use of foundation models (such as GPT, Claude, Gemini, LLaMA, or Mistral) for specific tasks (text generation, summarization, classification, information extraction, image generation, or code).
Fine-tuning, prompt engineering, or the use of in-context learning techniques to adapt generative models to specific use cases.
Comparison of capabilities and performance among different open-source and proprietary models (HuggingFace, OpenAI, Google Vertex AI, etc.).

Solution Development and Integration

Design of pipelines for generation and inference using GenAI (for example, bots, intelligent assistants, document generators, etc.).
Use of tools such as LangChain, LlamaIndex, Semantic Kernel, or other orchestrators to build GenAI applications.
Development of solutions based on retrieval-augmented generation (RAG) with the integration of external sources (databases, APIs, documents).
Integration of GenAI APIs (OpenAI, Cohere, Anthropic, etc.) into backend (Python) or frontend flows (for example, interfaces in Streamlit, Gradio, or web apps).

Evaluation, Security, and Governance

Design and execution of generation quality tests (prompt testing, hallucination rate, factual consistency).
Review of ethical aspects: biases, hallucinations, data privacy, and responsible governance of generative models.
Clear documentation of experiments, results, and learnings for the team.

What you'll get

Recorridos formativos específicos a medida en CIENCIA DE DATOS, AWS, GCP, AZURE, IBM, PYTHON, PYSPARK y SAS.

Para Máster Ciencia de Datos: Conocer el funcionamiento de un entono de trabajo real en el que se aborda la solución de proyectos relacionados con la Inteligencia de Negocio.
Tener la visión global de cuáles son los términos y conceptos habituales que se utilizan en el contexto de la Inteligencia de Negocio.
Participar en el desarrollo de soluciones en cualquiera de los componentes (online, batch y/o capa analítica). Colaborar en un Equipo de Trabajo multidisciplinar, con métodos y herramientas enfocados a este tipo de proyectos. Contribuir con el conocimiento específico de cada alumno, en la aplicación de algoritmos y técnicas relacionadas a casos de uso relacionados con el Machine Learning.

Perfil que buscamos

Buscamos personas cursando el Máster en IA, con un alto nivel de inglés (C1), motivadas por desarrollarse en un entorno tecnológico y dinámico.

Objetivos Generales:

Comprender y experimentar con las posibilidades actuales de la Inteligencia Artificial Generativa en tareas de texto, imagen, audio o código.
Participar en la evaluación crítica del impacto ético y de negocio de las soluciones basadas en GenAI.
Aprender buenas prácticas para la integración responsable, segura y eficiente de modelos generativos en productos reales.

Modelado y Aplicaciones de GenAI

Evaluación y uso de modelos fundacionales (como GPT, Claude, Gemini, LLaMA o Mistral) para tareas específicas (generación de texto, resúmenes, clasificación, extracción de información, generación de imágenes o código).
Fine-tuning, prompt engineering o uso de técnicas de in-context learning para adaptar modelos generativos a casos de uso específicos.
Comparativa de capacidades y rendimiento entre diferentes modelos abiertos y propietarios (HuggingFace, OpenAI, Google Vertex AI, etc).

Desarrollo e Integración de Soluciones

Diseño de pipelines de generación e inferencia usando GenAI (por ejemplo, bots, asistentes inteligentes, generadores de documentos, etc).
Uso de herramientas como LangChain, LlamaIndex, Semantic Kernel u otros orquestadores para construir aplicaciones GenAI.
Desarrollo de soluciones basadas en retrieval-augmented generation (RAG) con integración de fuentes externas (bases de datos, APIs, documentos).
Integración de APIs GenAI (OpenAI, Cohere, Anthropic, etc.) en flujos backend (Python) o frontales (por ejemplo, interfaces en Streamlit, Gradio o apps web).

Evaluación, Seguridad y Gobernanza

Diseño y ejecución de tests de calidad de generación (prompt testing, hallucination rate, factual consistency).
Revisión de aspectos éticos: sesgos, alucinaciones, privacidad de datos y gobernanza responsable de modelos generativos.
Documentación de experimentos, resultados y aprendizajes de forma clara para el equipo.

This job was automatically translated to English, .

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