AI Engineer

• - Lead and mentor a cross-functional engineering team, fostering a culture of collaboration, innovation, and continuous improvement, with a strong focus on AI adoption.
• - Drive the design, implementation, and evolution of AI-powered solutions, particularly AI agents, to enhance software development workflows, developer productivity, and operational efficiency.
• - Act as a hands-on technical leader, actively contributing to system design, code reviews, and the development of AI-driven capabilities integrated into the platform.
• - Identify and prioritize high-impact opportunities to leverage AI across the software development lifecycle, including code generation, testing, debugging, incident analysis, and documentation.
• - Architect and implement scalable and secure systems that combine traditional backend engineering with AI components (e.g., LLM integrations, RAG pipelines, agent-based workflows).
• - Collaborate with product, engineering, and business stakeholders to define AI use cases aligned with company goals and deliver measurable impact.
• - Establish best practices for building, evaluating, and maintaining AI systems in production, including observability, performance, cost control, and reliability.
• - Drive the adoption of AI tools and practices across engineering teams, acting as a mentor and enabler for developers to effectively leverage AI in their daily work.
• - Ensure that AI implementations comply with security and regulatory requirements (e.g., PCI DSS, GDPR, DORA), especially in the context of sensitive financial data.
• - Mitigate technical risks related to both traditional systems and AI components, ensuring platform stability, resilience, and trustworthiness.
• - Contribute directly to the development of high-quality, high-performance software solutions, maintaining a strong hands-on involvement.
• - Diagnose and resolve complex issues in production environments, including those involving AI systems and agent-based workflows.
• - Drive technical decision-making, balancing innovation in AI with long-term scalability, maintainability, and business impact.
• - Drive technical decision-making, balancing long-term scalability with immediate business needs.
• - Foster a culture of innovation, collaboration, and accountability across technical teams.

AI Engineering Team Lead

• Build and deploy a wide range of AI solutions end-to-end
• Own the full lifecycle of every solution
• Collaborate closely with the AI Platform team
• Communicate progress and findings clearly
• Produce lightweight documentation and run enablement sessions

Applied AI Engineer – AI Enablement

• Construir los agentes y componentes PMAI de la plataforma: analizador estático del legacy, generador de código Java/Spring, refactorizador, catálogos de equivalencia y conversores de datos.
• Implementar el ciclo completo de modernización asistida por IA: extracción del árbol de dependencias COBOL/JCL/copybooks → análisis funcional → generación de Java/Spring/Spring Batch → integración con Oracle.
• Diseñar y orquestar pipelines multi-agente (LangChain / LangGraph / similar) sobre el árbol de dependencias del proyecto.
• Definir la estrategia de chunking, retrieval y optimización del contexto para bases de código grandes (millones de líneas COBOL).
• Realizar prompt-engineering y fine-tuning específico para tareas de conversión (COBOL → Java, JCL → Spring Batch, DB2 → Oracle).
• Integrar la plataforma con LLM providers (Claude, OpenAI/Codex, GitHub Copilot, modelos open-source) y con plataformas transversales BBVA.
• Construir herramientas internas, scripting y UI ligera para que los Pods de Conversión operen la plataforma.

Senior GenAI / PMAI Engineer – Legacy Modernization

• Diseño, desarrollo y evolución de sistemas de IA generativa en producción
• Integración de modelos y agentes con plataformas y sistemas de negocio
• Colaboración como referente técnico senior

• Rol de AI Engineer en un importante cliente del sector consumo
• Diseñar e implementar agentes conversacionales
• Trabajar en la integración con sistemas backend y plataformas de mensajería
• Diseñar y optimizar flujos de trabajo basados en IA

Senior AI Engineer

• Análisis de necesidades de negocio y definición de soluciones apoyadas en IA
• Diseño de flujos automatizados mediante herramientas de IA generativa
• Diseño, iteración y mantenimiento de prompts
• Soporte a equipos no técnicos en adopción de IA
• Evaluación de calidad de outputs de modelos LLM
• Participación en la implementación junto a equipos multidisciplinares

AI Engineer – Semipresencial Sevilla

• Build, deploy, and operate AI-driven solutions in production.
• Collaborate openly within the team.
• Communicate decisions, assumptions, and trade-offs clearly to the team.
• Experiment, validate quickly, and iterate based on frequent stakeholder and team feedback.
• Optimize for Analytics and Trading adoption, clarity, and trust.
• Make sensible trade-offs to deliver usable value early.
• Adapt as the team and project scale in communication, scope, and technical direction.

• Diseñarás arquitecturas escalables, seguras y rentables —principalmente sobre Azure
• Ejercerás un liderazgo técnico, interno y externo
• Convertirás necesidades de negocio en arquitecturas escalables, seguras y rentables sobre Azure
• Definirás estrategias de CI/CD con Azure DevOps y GitHub Actions
• Diseñarás integraciones con APIs REST

AI Architect

• Diseñar y desarrollar soluciones de IA/ML end-to-end (desde el prototipo hasta producción).
• Definir la arquitectura del sistema (modelos, servicios, pipelines de datos, inferencia en tiempo real/batch).
• Entrenar, evaluar y mejorar modelos (feature engineering, experimentación, métricas, análisis de errores, mitigación de sesgos).
• Industrializar modelos (MLOps): versionado, reproducibilidad, CI/CD, despliegue, monitorización, retraining, gestión de drift.
• Optimizar rendimiento y costes (latencia, throughput, cuantización, batching, ...).
• Asegurar calidad y gobernanza: trazabilidad, documentación, cumplimiento (privacidad, seguridad, políticas internas, auditoría).
• Contribuir a la calidad del código y a las buenas prácticas del equipo, apoyando a perfiles junior y mid.

• Advanced Prompt Engineering: Designing complex, dynamic prompt templates with conditional logic and efficiently reusing information and context within prompts to maximize generation quality and reasoning.
• Structured Outputs & Schemas: Implementing various response schemes (JSON mode, function calling, Zod/JSON schemas) to ensure AI outputs are predictable and ready for seamless integration into application logic.
• Prompt Engineering & Evaluations: Building robust evaluation pipelines and using Langfuse to collect feedback and score the quality of responses in real time.
• Tracing & Debugging: Performing deep debugging of complex LLM chains using Langfuse traces to identify bottlenecks and optimize for cost, latency, and context window usage.
• AI A/B Testing: Running systematic experiments across different models via OpenRouter (e.g., comparing Claude 3.5 Sonnet vs. GPT-4o) and analyzing results based on quantitative metrics.
• Data-Driven Decisions: Making deployment decisions for new prompts or models strictly based on quantitative benchmarks and trace data, rather than intuition.
• Output Scoring & Analysis: Developing scoring systems to analyze the “Problem → Solution” chain and identify root causes of hallucinations or logic errors using Langfuse analytics.
• Model Performance & Fine-Tuning: Regularly re-evaluating model performance as new architectures emerge and performing fine-tuning when necessary to meet specific domain requirements.

• Operate & Maintain AI Systems: Ensure reliability, scalability, and observability of AI-powered services deployed on AWS, including LLM-based systems and agentic workflows used across the organization.
• Architect Agent Behavior: Design and version-control complex system prompts, ensuring agents have clear personas, robust guardrails, and precise tool definitions.
• Curate Knowledge Context: Manage the "Golden Corpus" for our agents and RAG systems, optimizing data chunking and metadata strategies to ensure accurate information retrieval and proper execution context.
• Architect & Optimize AI Workflows: Design and continuously improve prompt libraries, skills repositories, orchestration frameworks, and automation pipelines that power internal AI tools.
• Model Evaluation & Benchmarking: Evaluate and compare AI models (LLMs and foundation models) across quality, latency, cost, safety, and robustness.
• Implement Automated Evals: Build "Ground Truth" datasets and design "LLM-as-a-Judge" pipelines to rigorously test agent performance before deployment.
• Enablement & Best Practices: Provide reusable components, documentation, and operational standards that empower engineering teams and internal stakeholders to safely leverage AI capabilities.
• Experimentation & Continuous Improvement: Drive structured experimentation cycles (A/B testing, offline evals, shadow testing) to iteratively improve system performance.
• Governance & Guardrails: Implement versioning strategies, access controls, auditability, and responsible AI guardrails aligned with a regulated fintech environment.

• You will help improve our existing ML-based products and build new features and services using LLMs.
• A core part of the role is context engineering — designing the right inputs, retrieval strategies, and prompt structures to get the best possible output from LLMs in production.
• You'll also contribute to our software engineering efforts when needed, bridging the gap between ML and software engineering on the team.
• You will work in the Machine Learning Team, where we build LanguageWire's machine translation (MT) and adjacent services to monitor and predict its performance.
• The team consists of ML Engineers, Analytics Engineers, and Full Stack Software Engineers.
• You'll collaborate across these disciplines daily, and we value people who can move fluidly between ML experimentation and production engineering.
• You'll report to the team leader.

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AI Engineering Team Lead

Applied AI Engineer – AI Enablement

Senior GenAI / PMAI Engineer – Legacy Modernization

AI Engineer

Senior AI Engineer

AI Engineer – Semipresencial Sevilla

AI Engineer

AI Architect

AI Engineer

AI Engineer

Senior AI Engineer

AI Engineer