Role Description Capgemini Engineering, líder mundial en servicios de ingeniería, reúne a equipos de ingenieros/as, científicos/as y desarrolladores/as para ayudar a las compañías más innovadoras a liberar su potencial y construir un futuro mejor. Únete al equipo y continúa tu carrera en una compañía con oportunidades reales de crecimiento, donde puedes marcar la diferencia y donde ningún día es igual al anterior. Estamos buscando perfiles con experiencia en Inteligencia Artificial, Machine Learning y Data para incorporarse a un proyecto internacional con sede en Málaga pero con opción real de trabajo remoto. Se requiere capacidad para desenvolverse con soltura en proyectos de IA aplicada, análisis avanzado y modelado. - Participar en el diseño y desarrollo de soluciones basadas en Machine Learning y analítica avanzada. - Aplicar técnicas de Inteligencia Artificial para resolver problemas complejos sobre grandes volúmenes de datos. - Preparar, limpiar y transformar datos, asegurando calidad, consistencia y trazabilidad. - Construir y optimizar pipelines de datos que alimenten modelos o flujos analíticos. - Experimentar con modelos, evaluar resultados y documentar el ciclo de vida de las soluciones. - Integrarse en equipos multidisciplinares colaborando con perfiles técnicos y funcionales. - Trabajar en entornos ágiles manteniendo buenas prácticas de versionado y documentación. - Participar en proyectos de innovación y adoptar nuevas herramientas/tecnologías cuando sea necesario. Qualifications - Experiencia laboral previa trabajando con Machine Learning, Inteligencia Artificial o análisis de datos aplicado. - Conocimientos sólidos en Python y librerías de análisis (pandas, NumPy) o ML (scikit‑learn, TensorFlow, PyTorch). - Experiencia manipulando y modelando datos, ya sea en entornos cloud o locales. - Capacidad para desenvolverse con soltura en proyectos de IA, incluso sin un número específico de años. - Inglés mínimo B2 (no es necesario certificado). Requirements - Experiencia previa con LLMs, RAG, agentes o frameworks de GenAI. - Uso de herramientas cloud (Azure, AWS o GCP). - Experiencia con bases de datos SQL/NoSQL. - Conocimientos de MLOps (Docker, MLflow, CI/CD). - Background en tratamiento de datos para entornos productivos o industriales. - Conocimientos en Spark, PySpark o Databricks. Benefits - Contrato con Capgemini Engineering. - Participación en proyectos innovadores dentro del ámbito Aeroespacial / Space – Offshore. - Oportunidad de crecimiento profesional en áreas de IA aplicada y Data. - Modalidades de trabajo flexibles, incluyendo trabajo remoto. - Acceso a formación continua, certificaciones y tecnologías punteras. - Cultura orientada a resultados y aprendizaje continuo.

• Advance AI Safety: Design, implement, and evaluate attack and defense strategies for LLM jailbreaks (prompt injection, obfuscation, narrative red teaming) and deploy them as production-grade services. • Build Scalable Safety Infrastructure: Architect and deploy distributed safety evaluation pipelines handling millions of requests, with real-time monitoring, alerting, and incident response capabilities. • Large-Scale Data Engineering: Design ETL pipelines for processing terabytes of safety-related data (attack patterns, behavioral logs, model outputs); build data lakes and feature stores for safety ML systems. • Evaluate AI Behavior: Analyze and simulate human-AI interaction patterns at scale to uncover behavioral vulnerabilities, social engineering risks, and over-defensive vs. permissive response tradeoffs. • Agentic AI Security: Build production workflows for multi-agent safety (agent self-checks, regulatory compliance, defense chains) spanning perception, reasoning, and action. • MLOps & Model Deployment: Deploy safety models to production using containerized microservices, implement CI/CD pipelines for model updates, and manage model versioning and A/B testing infrastructure. • Benchmark & Harden LLMs: Create reproducible, automated evaluation protocols for safety, over-defensiveness, and adversarial resilience across diverse models with continuous integration.

• Collaborate with major clients and partners • Clearly explain technical concepts and propose solution recommendations in direct collaboration with major clients and partners. • Evaluate, monitor, and continuously improve the performance of our Retrieval-Augmented Generation (RAG) pipeline to ensure high-quality responses to legal queries. • Prototype, evaluate, and enhance our RAG pipeline to increase response accuracy, speed, and cost-efficiency. • Fine-tune large language models (LLMs) to ensure precision and relevance when answering legal questions. • Design, implement, and maintain robust ETL pipelines for seamless data transformation. • Optimize workflows for storing and managing parsed content in a vector database to enable efficient information retrieval. • Develop and improve embedding and chat models to optimize the user experience. • Work closely with the software engineering team to seamlessly integrate AI/ML components into the overall product architecture. • Deploy LLMs and associated models to production using Azure cloud infrastructure to ensure optimal performance and reliability. • Collaborate with the operations team to automate model deployments using Terraform and other CI/CD tools to ensure scalability and efficiency.

• AI Discovery & Solutioning: Understand energy customers' AI strategy and sector-specific challenges (e.g., predictive maintenance, outage management, supply chain resilience, or regulatory compliance). Find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle. • Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully reached. • Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex operational problems. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points specific to power generation, transmission, and O&G operations. • Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent field service routing or autonomous procurement). • Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise data, identity protocols, and stringent energy compliance frameworks. • Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the Energy vertical, scaling knowledge across the organization regarding utilities, upstream/downstream oil & gas, and renewable energy processes.