Machine Learning Engineer

• Build and contribute to RAG system components under senior guidance, with growing autonomy
• Write tests and help build evaluation harnesses
• Write production code across AI, backend services, and data pipelines
• Integrate AI components into backend services and RESTful APIs
• Support deployment of containerized systems to AWS using CI/CD
• Contribute to documentation, runbooks, and client handover materials
• Participate in technical discussions and architectural decisions
• Support model evaluation and investigate and improve failure modes
• Take increasing ownership of components and technical decisions

Junior AI/ML Engineer, GenAI, AWS

• Develop full-stack machine learning models, including binary classification and regression, for financial crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype solutions, and deploy models in production
• Own and evolve the Dynamic Risk Score across all markets
• Score the full client base daily to support decision-making across the client lifecycle
• Manage transaction rules with ML-driven detection and optimize their performance and alert volume
• Establish continuous monitoring of model and rule performance in production
• Develop a champion-challenger framework to keep the best-performing model live
• Contribute to infrastructure development for ML model operationalization and inter-service interactions
• Deploy, serve, monitor, and improve the production ML product

Senior Machine Learning Engineer

• Design, train, evaluate, and retrain machine learning models, especially recommender systems
• Support game features, player experience, and operational efficiency
• Own model performance monitoring, including drift, data quality, degradation, thresholds, and business health criteria
• Establish experimentation practices using model versioning and experiment tracking tools such as MLflow or Weights & Biases
• Establish model governance practices for production promotion and communication between MLOps and Data Science
• Create templates and cookiecutter scaffolding to standardise model and experiment structures, handoffs, and communication
• Build training and feature-engineering pipelines
• Write clean, production-ready Python and SQL
• Explore early-stage LLM, RAG, and agentic AI projects where valuable
• Develop simple interfaces such as Streamlit applications for non-technical users
• Collaborate with product, data, and engineering teams
• Communicate results, limitations, and recommendations to technical and business audiences

ML Engineer

• Own ML initiatives end to end, from understanding customer problems and designing experiments through implementation, deployment, measurement and continuous improvement
• Evaluate ML and AI technologies with a strong product mindset, prioritizing pragmatic solutions that deliver customer and business value
• Partner with product managers and engineers to design and deliver robust, production-ready ML and AI solutions
• Write clean, efficient and maintainable code using strong software engineering practices
• Work collaboratively through regular pair programming, knowledge sharing and continuous improvement of engineering practices
• Provide technical leadership for machine learning initiatives from problem definition and experimentation through production delivery
• Coach and mentor engineers, raising ML capability through pairing, knowledge sharing and technical guidance

• Design and build core machine learning systems for a proactive AI platform
• Own projects end-to-end, including data curation, model training, evaluation, inference, and continuous iteration
• Translate research concepts into robust, reliable production systems
• Use live production signals and data to troubleshoot model anomalies and system bottlenecks
• Deploy systems, analyze real-world performance metrics, refine approaches, and iterate rapidly
• Collaborate with research, engineering, and product teams to connect technical milestones with user value
• Mentor fellow engineers and conduct rigorous code and technical reviews
• Optimize model architectures for latency, computational cost, reliability, and safety at scale

• Diseñar, entrenar, calibrar y monitorizar modelos predictivos de churn
• Desarrollar soluciones analíticas avanzadas para identificar riesgos de abandono y oportunidades de optimización de cartera
• Transformar resultados analíticos en segmentaciones, priorizaciones y recomendaciones accionables
• Impulsar campañas de retención en colaboración con los equipos de P&C y Gestión de Cartera
• Actuar como enlace entre negocio y analítica
• Validar hipótesis de negocio y contribuir a decisiones basadas en datos
• Supervisar la calidad de los datos y los pipelines que alimentan los modelos
• Analizar el rendimiento de modelos y campañas, identificando oportunidades de mejora, reentrenamiento y optimización
• Contribuir a la evolución de prácticas de MLOps y a la industrialización de modelos en entornos cloud

• Build and scale the AI/ML infrastructure and pipelines powering Aily’s decision intelligence platform
• Build and maintain ML systems and pipelines from data through model, deployment, and monitoring
• Support productization of AI/ML models and LLMs through APIs and infrastructure
• Apply software engineering best practices including testing, CI/CD, and versioning
• Collaborate with data scientists and engineers to deliver scalable solutions
• Monitor systems in production and communicate progress and technical challenges
• Design and implement end-to-end ML pipelines autonomously
• Contribute to APIs, tools, and libraries used daily by more than 100 AI team members
• Present results and findings, identify gaps and opportunities, and explain the product and underlying software/AI
• Mentor junior team members on technical topics

Machine Learning Engineer jobs in Spain

Junior AI/ML Engineer, GenAI, AWS

Senior Machine Learning Engineer

ML Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

ML Engineer

Mid AI/ML Engineer