Top Artificial Intelligence Jobs in Spain

Lead design, build, and production deployment of AI solutions across Arxada's Microsoft ecosystem. Own architecture for Azure AI, Microsoft Fabric, M365 Copilot, and Power Platform; integrate OT edge-to-cloud pipelines; embed security, identity, and responsible AI controls; manage MLOps and lifecycle; and mentor engineers and data scientists to deliver scalable enterprise AI solutions.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Create short- and long-form AI-generated educational and entertainment content for Instagram, TikTok, and YouTube. Rapidly test content hypotheses, analyze performance, research trends and competitors, and iterate on hooks, storytelling, and editing. Collaborate with the product team and continually experiment with AI image/video generation and editing tools.

The Senior AI Architect will lead AI architecture design to enhance efficiency and transformation, serving as a technical authority to scale AI adoption effectively across teams.

Design, build and deploy AI-powered solutions (custom copilots, conversational agents, embedded AI) using Microsoft Copilot Studio, Power Automate and Azure AI Foundry. Integrate with Snowflake and enterprise systems, collaborate with stakeholders to define use cases, operationalise models, maintain reliability and governance, and evaluate emerging AI tools.

The role involves building robust backend systems for seQura's Smart Agent platform, integrating AI capabilities, and collaborating cross-functionally to enhance the shopper experience.

Lead design, prototyping, and production deployment of AI-driven productivity tools across finance, HR, operations, sales, and marketing. Embed with business teams, build scalable LLM/agentic AI solutions, create reusable platforms, ensure reliability, governance, and cross-regional adoption, and mentor engineering teams.

The Senior Software Engineer will develop backend services for Workbot, engage in research, contribute through the development lifecycle, and enhance existing systems.

Partner with teams to identify high-impact AI use cases, rapidly prototype and deploy AI and agent-based solutions, coach teams for self-service adoption, build training/playbooks, scale reusable patterns, and monitor adoption and outcomes to optimize enterprise AI capability.

Lead product vision and roadmap for Cint's Exchange AI Demand services. Conduct opportunity assessments, define product requirements, prioritize features using data and customer research, and partner cross-functionally to deliver API and UI-driven AI products, aligning stakeholders and enabling go-to-market success.

The AI Platform Engineer will design and manage Kubernetes environments for AI workloads, automate infrastructure, and optimize computing resources in secure settings.

Lead and build Maze’s new AI-native Market Research service: define research vision and methodologies, recruit and manage a research team, own delivery quality, partner with Product/Engineering and GTM, and serve as executive research lead on strategic customer engagements.

Embedded in a consulting team, design and implement Python-based AI data pipelines, integrate LLM APIs, build and deploy LLM-powered agents and MCP servers, create reusable automation and multi-agent systems, translate business problems into AI solutions, support project scoping, and communicate technical work to non-technical stakeholders across healthcare and life sciences projects.

Lead enterprise AI strategy, governance, and delivery for regulated life sciences. Own end-to-end AI/data architectures (Azure, Snowflake), mentor engineers, implement MLOps, ensure compliance, and drive adoption and measurable business outcomes.

Lead execution of the AI transformation strategy: prototype and scale AI-powered services, own revenue for innovation offerings, build repeatable piloting frameworks, implement AI agents internally, and partner cross-functionally to drive commercial impact and operational efficiencies.

Lead execution of the AI transformation strategy, prototype and scale AI-powered services, own revenue targets for innovation offerings, build operational frameworks and governance, and drive internal AI adoption to improve efficiency and margins while partnering cross-functionally with Sales, Product, and Delivery.

Own client success for Emiri deployments by triaging issues, identifying root causes, coaching commercial teams, configuring and QA-ing client models, testing fixes, and translating user feedback into product enhancements and clear guidance for users and development.

Design, build, and operate enterprise-grade AI agents and agentic workflows. Integrate multiple LLM providers, implement retrieval/orchestration/tool invocation, and deliver cloud-native, containerized services (Kubernetes/Docker) with CI/CD, observability, and production support. Partner with clients to define use cases, prototype, deploy, measure KPIs, and share reusable patterns and documentation.

Design, build, and run production-grade agentic AI systems for enterprise clients: architect multi-LLM integrations, RAG pipelines, orchestration, LLMOps, and cloud-native deployments. Lead client engagements, set engineering standards, measure system metrics, and share reusable patterns to scale agentic solutions across verticals.

Design, build, and deploy production-quality AI/ML solutions (NLP & Computer Vision). Own end-to-end ML lifecycle including data cleaning, model development, MLOps, automated deployment, monitoring, testing, and optimization in cloud environments.

Contribute to data collection, cleaning, quality checks, and basic pipeline monitoring. Support documentation, collaborate with engineers and analysts, and learn SQL, Python, Git, and modern data warehousing tools.

Own the end-to-end AI-powered content process: develop prompts, generate and edit SEO-friendly articles, fact-check, optimize for search, repurpose content, maintain editorial guidelines, and publish via CMS while improving AI workflows and collaborating with writers and SEO specialists.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Own end-to-end delivery of complex AI engagements as a forward-deployed engineer: translate business needs into technical solutions, rapidly prototype agentic AI systems, deploy production-ready solutions, optimize performance and reliability, capture field learnings, and contribute to platform evolution while partnering closely with customers.