Top Machine Learning Jobs in Spain

Design, deliver, and continuously improve scalable production AI solutions using LLMs, RAG, agentic AI, machine learning, and document intelligence. Build cloud-native systems on Azure using software engineering, architecture, DevOps, and MLOps practices. Own AI platforms and models across deployment, monitoring, security, governance, and optimization. Lead technical initiatives, collaborate with product and business teams, mentor engineers, and translate business needs into measurable outcomes.

Lead and develop globally distributed Data and AI teams to deliver production Data/AI solutions. Drive talent development, operational excellence, stakeholder alignment, and responsible AI practices while partnering with Product and Engineering to set priorities, roadmaps, and delivery metrics.

Leads an international data science and AI team of up to eight members focused on commercial analytics and AI accelerators. Owns demand intake, prioritization, product strategy, model and data product delivery, and enterprise-scale ML/AI platform development. Partners with commercial, marketing, finance, and brand leaders to translate advanced analytics into business recommendations, revenue growth, and resource optimization. Requires executive communication, team-building, product management, and hands-on expertise in ML, LLMs, agentic workflows, cloud deployment, and pharma or healthcare analytics.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Lead planning, deployment, optimization and operational support of CrowdStrike Falcon AI/ML detection and response capabilities. Implement and tune ML models, build automation playbooks, integrate Falcon with customer tooling, develop scripts using Falcon APIs, support incident response and threat hunting, and deliver customer workshops and collateral.

Join the Orbit 12-month graduate program as an Associate Applied AI Engineer supporting customer engagements: uncover business challenges, prototype ML and LLM solutions on the Celonis Process Intelligence Platform, deliver proof projects and executive presentations, drive value realization, collaborate with product and engineering, and travel to meet clients.

Join Celonis' Orbit program to support Benelux customers as an Associate Applied AI Engineer. Deliver proofs, prototype ML and LLM-based solutions on the Celonis Process Intelligence Platform, present quantified business value to executives, drive change management, and collaborate with Product and Engineering to scale reusable AI solutions.

Join Celonis's 12-month Orbit graduate program as an Associate Applied AI Engineer (Value Engineer). Support DACH customer engagements, prototype ML and LLM-based AI solutions on the Celonis Process Intelligence Platform, quantify business value, present to executives, drive change management, and collaborate with product and engineering to scale reusable AI solutions.

Lead Essity’s AI technical architecture strategy and roadmap, translating business goals into scalable end-to-end AI solutions. Partner with business leaders, data scientists, AI engineers, and technology providers to prioritize and pilot use cases. Evaluate AI technologies, conduct data/model/software audits, ensure compliance with AI risk standards, and support model recalibration and retraining.

Leads cybersecurity and technology risk for Santander’s Data & AI division. Advises executives on cyber risk, resilience, security investments, and security posture; oversees governance, regulatory compliance, controls, reporting, audits, and remediation. Partners with Risk, Compliance, Legal, Audit, Privacy, Technology, and business stakeholders. Ensures security-by-design, critical data protection, threat mitigation, and secure, responsible deployment of AI capabilities in alignment with group strategy and regulatory expectations.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Own Afori’s machine-learning reasoning systems for insurance agents. Responsibilities include designing continuous-learning loops, fine-tuning and distilling open-weight and proprietary LLMs for German insurance NLP, building cross-sell intelligence models, and establishing evaluation standards, benchmarks, abstention checks, faithfulness checks, and production serving infrastructure. The role requires shipping cost-effective ML systems at scale in a regulated, early-stage environment.

Lead end-to-end process analysis using process mining and analytics to identify inefficiencies and automation opportunities. Translate business needs into actionable use cases, build process semantics and data foundations, implement scalable solutions, and promote adoption of Process Intelligence and AI-driven automation across functions.

Own an AI neobank product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Incorporate regulatory requirements across KYC, monitoring, limits, privacy, and licensing. Apply AI with appropriate evaluation, safeguards, oversight, and reliability controls while aligning engineering, design, compliance, operations, treasury, and finance.

Collaborate with clients and consulting teams to solve complex business challenges through quantitative analytics. Responsibilities include designing analytical solutions, developing prototypes, applying machine learning and NLP models, validating GenAI tools, modeling financial risk, optimizing promotions, and assessing solution effectiveness. The role involves communicating sophisticated data science concepts clearly, working across industries and international projects, and continuously expanding technical expertise.

Develop analytical and machine learning solutions for global consulting clients across financial services, energy, retail, transportation, and other industries. Responsibilities include designing solutions, building prototypes, evaluating effectiveness, developing NLP and predictive models, applying advanced analytics, and communicating data science concepts to clients and colleagues. The role offers coaching, international exposure, and hybrid collaboration.

Build and operate data and AI governance systems, including data quality testing, catalogs, metadata, lineage, ownership frameworks, model inventories, and risk workflows. Partner with data, engineering, legal, privacy, security, and business teams to support GDPR, SOC 2, and EU AI Act readiness. Enable self-service analytics and responsible AI through documentation, access governance, audits, guardrails, and automated quality monitoring.

Analyze fraud data to identify emerging trends and risks, develop machine learning and AI solutions, enhance fraud copilot tools, and identify automation opportunities. Translate business needs into analytical approaches, maintain reusable version-controlled code, and communicate actionable insights to technical and non-technical stakeholders.

Leads AI and data science projects applying machine learning, geospatial analysis, economic modeling, and predictive analytics to public policy and economic challenges. Responsibilities include designing data pipelines, developing dashboards and models, translating findings into policy recommendations, managing end-to-end workflows, mentoring data scientists, and ensuring code quality and documentation.

Combine AI, machine learning, geospatial analysis, and economic modeling to transform satellite imagery, trade data, and macroeconomic indicators into actionable policy intelligence. Design automated data pipelines, interactive dashboards, rankings, and strategy tools for governments, institutions, and corporations. Responsibilities also include understanding complex problems, developing technical solutions, communicating findings clearly, and applying predictive modeling, LLMs, and simulation technologies to real-world challenges.

Design, build, and maintain scalable ML infrastructure, data pipelines, and model serving for batch and real-time systems. Optimize compute resources and monitoring, implement MLOps and CI/CD, collaborate with ML scientists, research new technologies, and mentor junior engineers.

Lead architecture, build, and operate end-to-end ML infrastructure for training, deployment, and serving. Drive ML Ops practices, CI/CD, observability, scalable data pipelines, model serving, and reliability. Partner with ML scientists, mentor engineers, and evaluate technologies to deliver production-grade ML systems at scale.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Lead end-to-end architecture, implementation, and delivery of Blue Yonder Warehouse Management and related execution solutions. Serve as the primary client contact, manage timelines, budgets, risks, and deliverables, and lead solution teams and integrations. Provide functional and technical guidance, mentor junior staff, align solutions with business objectives, and collaborate with project managers, delivery partners, and stakeholders to deliver successful client outcomes.