Top Artificial Intelligence Jobs in Spain

Develop and deploy end-to-end AI solutions for a critical public-sector project. Responsibilities include building generative AI, RAG, agentic, AIOps, and LLMOps solutions; designing scalable architectures with vector databases; integrating AI with ITSM, BMC, CMDB, and observability platforms; and estimating costs, performance, and scalability. The role requires at least five years of experience, software engineering expertise, programming skills, and knowledge of microservices, containers, and messaging systems.

Lead AI strategy and adoption across Corporate Functions at DWS. Build prototypes and production-oriented solutions using LLMs, RAG pipelines, and agentic workflows; coach engineering and business teams; run AI adoption programs and a community of practice; evaluate models and tooling; and advise leadership on governance, roadmaps, responsible AI, and financial-services use cases.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, roadmaps, success metrics, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify financial workflows; use research, feedback, metrics, and experiments to improve products; and drive rapid launches and post-launch iteration.

Lead AI strategy and adoption across corporate functions by coaching teams, facilitating communities of practice, identifying use cases, and advising leadership. Build and demonstrate RAG pipelines, LLM applications, agentic workflows, prototypes, and integrations. Evaluate AI tools and platforms, guide responsible adoption in regulated financial services, translate capabilities into business value, and turn experimentation into sustained organizational change.

Develop and deploy AI-powered capabilities across product and business teams. Act as a forward-deployed engineer, embedding with finance, sales, marketing, and product teams to introduce agentic methodologies, support AI adoption, shape the company platform and strategy, and translate learnings into scalable solutions. The role emphasizes production-quality code, business impact, adaptability, collaboration, and effective work in a global distributed environment.

Own AI stockbroking and other financial product areas from user problem discovery through launch and iteration. Define product direction, scope, metrics, roadmaps, and launch plans while partnering with engineering, design, data, operations, compliance, and business teams. Simplify complex financial workflows, use research and performance data to guide decisions, manage trade-offs, and drive continuous improvement after release.

Own AI investing and other financial product areas from user problem definition through launch and iteration. Define scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams. Simplify complex financial workflows, use research and performance data to guide decisions, and balance speed, customer experience, business impact, compliance, and operational complexity.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Drive enterprise sales of Microsoft Modern Work, AI, Copilot, and cloud solutions in German-speaking Switzerland. Identify customer needs, build relationships with business decision-makers, lead value-based solution selling, coordinate internal specialists and partners, and manage opportunities through forecasting, competitive planning, cross-selling, and closing. The role requires strong German communication skills, technology-related sales or account management experience, and the ability to sell solutions to enterprise customers.

Own product areas within an AI neobank, defining user problems, roadmaps, success metrics, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify financial products, ship features, monitor performance, and iterate based on data, feedback, research, and production issues. Balance speed, user experience, business impact, compliance, and operational complexity while driving execution with high ownership.

Own product areas within BJAK’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, roadmaps, metrics, scope, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; launch features; monitor performance; and iterate using data, research, feedback, and production insights.

Own product areas across financial services, including insurance, payments, savings, investing, and travel. Define user problems, product scope, success metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to ship and improve products. Use metrics, feedback, research, and production insights to guide decisions while balancing speed, user experience, business impact, compliance, and operational complexity.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, roadmaps, metrics, scope, and launch plans; collaborate with engineering, design, data, compliance, operations, and business teams; simplify complex financial journeys; launch features; monitor results; and iterate using data, feedback, research, and production insights.

Own the Platform task force roadmap for internal tools, automation, AI agents, security, compliance, reliability, and operational scalability. Map internal workflows, prioritize automation and legacy-system migrations, design permissions and auditability, and measure time saved, cost reduction, risk reduction, and adoption. Partner with Engineering, Ops, Finance, Legal, Data, Care, and product design to align stakeholders and deliver impactful internal products.

Own product areas within AI stockbroking and other financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial products; launch features; monitor performance; and drive ongoing improvements using data, feedback, research, and production insights.

Own product areas within AI Finance across insurance, payments, savings, investing, travel, and other financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial products; and use data, feedback, research, and production insights to launch, monitor, and continuously improve products.

Develop and optimize GPU- and CPU-accelerated numerical software libraries for AI, analytics, image processing, computer vision, and scientific simulation workflows. Responsibilities include designing scalable algorithms, performance tuning, benchmarking across architectures, collaborating on feature requirements and technical roadmaps, providing technical leadership, and improving library usability and performance.

Leads the design, development, testing, optimization, troubleshooting, and maintenance of complex full-stack software applications. Collaborates with cross-functional teams, documents technical requirements, resolves IT issues, supports end users, and applies modern engineering practices including microservices, APIs, cloud messaging, CI/CD, containers, Kubernetes, monitoring, and Agile methodologies.

Leads the design, development, maintenance, testing, troubleshooting, and optimization of complex full-stack software applications. Collaborates with cross-functional stakeholders, prepares technical documentation, resolves technical issues, supports end users, and preprocesses data. The role uses C#, .NET, Angular, APIs, microservices, relational databases, messaging systems, CI/CD, containers, monitoring tools, and Agile practices.

Leads the design, development, maintenance, testing, optimization, and troubleshooting of complex full-stack software applications. Collaborates with cross-functional stakeholders, produces technical documentation, prepares data, resolves IT issues, and provides end-user support. The role requires advanced C# and .NET expertise, Angular, APIs, microservices, databases, messaging systems, cloud-oriented engineering practices, CI/CD, containerization, monitoring, and Agile methodologies.

Own the architecture and production delivery of a real-time voice AI copilot. Lead streaming speech-to-text, LLM extraction, Chrome extension integration, AWS infrastructure, latency optimization, evaluation, observability, cost control, concurrency, data isolation, monitoring, and rollback. Lead the AI engineering pod, manage client stakeholders and scope, present evidence-based results, deliver production epics, and transfer knowledge to client and internal teams.

Lead end-to-end development of an AI mahjong companion, including the gameplay decision model, LLM explanation layer, valid-action integration, win detection, training pipeline, evaluation harness, and low-latency serving. Own architecture, AWS deployment, Langfuse observability, and the two-second response budget while leading a small AI pod and communicating technical decisions to the client’s CTO and engineering team.

Design and implement enterprise AI and Agentic AI architectures that are scalable, secure, reliable, governable, and maintainable. Translate business and technical requirements into architectures spanning AI agents, foundation models, data platforms, APIs, orchestration, observability, security, and cloud infrastructure. Integrate AI solutions with enterprise systems, guide implementation and architecture reviews, evaluate emerging technologies, promote responsible and sustainable AI, and help transition experiments into production-grade solutions.

Build and improve applied AI, agent-based, machine learning, and LLM solutions for client and internal use cases. Responsibilities include data exploration, prototyping, model validation, analytical pipeline development, decision logic, documentation, and continuous improvement. The role collaborates with data scientists, engineers, architects, and cross-functional teams while supporting practical deployment and scalable adoption of AI solutions.

Build reliable datasets and production-grade SQL data models, applying data quality, testing, documentation, and version-control practices. Collaborate with analysts, product managers, and business stakeholders to automate workflows and deliver trusted data products. Use generative AI responsibly for coding, analysis, workflow automation, and data experiences while validating outputs. Contribute to data transformations and progressively develop ownership of small data products and AI-enabled analytics initiatives.