Top Artificial Intelligence Jobs in Spain

As a Finance AI Application Specialist, you'll manage and optimize financial systems, implement AI solutions, collaborate with finance teams, and provide support.

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.

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.

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.

Lead the design of scalable data and AI solution architectures on Google Cloud Platform, supporting implementation, troubleshooting, and optimization of data initiatives.

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.

The Senior ML Engineer will create and improve ML models, collaborate with teams, monitor models in production, and maintain documentation.

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 AI Engineer will design, build, and deploy AI solutions, focusing on data analytics and machine learning, and collaborate with interdisciplinary teams.

Lead the development of computer vision systems for scene understanding, manage technologies, mentor engineers, and stay updated on AI trends.

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.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

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 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.

Lead and operate the HR tech stack (HiBob, Greenhouse), design and implement integrations and automations, build People data models and BI dashboards, drive AI/LLM adoption with governance, and act as internal product owner to eliminate manual workflows and improve People operations.

Lead enterprise AI and cloud sales conversations, manage territory planning and forecasting, collaborate with partners and stakeholders, accelerate deal closure, and run workshops and seminars to drive customers’ digital transformation on Microsoft Azure.

Lead On-Demand AI and technical projects: own partner onboarding, manage API integrations, resolve tech issues, identify process inefficiencies, and build AI-driven automation using data and SQL.

Design and implement backend business logic and orchestration for AI coding agents, integrate agents with product surfaces, external APIs and CLIs, build highly concurrent asynchronous workflows, improve context handling and failure recovery, collaborate with product and ML teams, and own technical design and code quality.

Design, prototype, and ship AI-powered features and agent/LLM workflows for analytics and large datasets. Rapidly iterate with user feedback, integrate agentic components with internal tools, and ensure scalable, production-grade solutions while collaborating across the team.

Lead research and engineering for large-scale LLM and multi-modal pre-training: design architectures and tokenizers, curate massive datasets, run distributed training on thousands of GPUs, debug performance bottlenecks, and improve cross-modal alignment and efficiency.

Freelance AI evaluator interacts with AI chatbots and language models (written and voice), performs structured testing, submits ratings and deliverables, and completes language and technical assessments.

Support AI discovery and adoption across Wealth Management & Insurance by running workshops, designing and refining prompts, documenting use cases and process changes, preparing training and enablement materials, tracking adoption and impact, and coordinating with Data & AI, Technology, Risk, Compliance and business stakeholders.

Lead AI transformation for Wealth Management & Insurance by identifying and prioritizing AI use cases, designing Copilot/agent/automation solutions, running discovery workshops, building business cases and KPIs, coordinating delivery with data, tech and control teams, and driving adoption and responsible AI practices across the business.

Embed with clients to design, build, and run production agentic AI systems. Architect retrieval, orchestration, policy routing, tool invocation, evaluation harnesses, and lifecycle observability. Integrate multiple AI providers, leverage cloud-native patterns (Kubernetes, microservices, serverless), implement CI/CD and IaC, run workshops/POCs, measure agent performance, and create reusable patterns and documentation.

Design, build, and operate an internal AI Background Agents Platform: implement secure stateful runtimes, sandboxing, CI/CD and GitOps pipelines, progressive delivery, observability (tracing/monitoring), and developer-facing tools to enable teams to build, test, and deploy autonomous LLM-backed agents.