Top Artificial Intelligence Jobs in Spain

Work with global commercial stakeholders to deliver BI, forecasting, machine learning, and LLM-based solutions. Build and automate data pipelines, design dashboards, produce clear insights for executives, and manage multiple analytics projects to drive growth, retention, and client experience.

Lead short-term AI adoption program: build internal AI champions, create usage policies and training, support teams implementing AI tools, contribute to internal AI tool builds, and ensure responsible, secure deployment across distributed teams.

Own delivery from spec to production using AI coding agents. Define verification, review and accept agent-generated code, tune agent toolchain, step in to hand-code (mainly Go), and ensure spec->code->test traceability.

Lead prompt engineering initiatives and provide technical enablement to business users: develop prompt best practices, coach and train stakeholders, integrate AI tools into workflows, evaluate AI platforms, mentor junior staff, and document technical learnings and case studies.

Lead Hopper's finance analytics, data warehouse, BI and AI enablement strategy. Own data pipelines, GCP cost optimization, finance AI automation, reporting COE, data governance, and audit controls. Build self-service analytics, establish model foundations and manage a cross-functional team partnering with FP&A, accounting, and engineering.

Lead prompt engineering initiatives, coach and support business users on AI tools, create best practices and training, evaluate AI platforms, mentor junior staff, and document technical learnings to integrate AI into workflows.

Lead definition and execution of AI adoption strategy across Savings & Investment Domain. Identify AI opportunities, coordinate deployment of global AI products, align country execution, drive change management, monitor adoption metrics, and promote AI literacy to maximize business value.

Trainee will build and train machine learning models, design algorithms (NLP and computer vision), collaborate with cross-functional teams, and apply AI tools (TensorFlow/PyTorch/AWS) to optimize industrial processes.

Lead end-to-end implementations of QVAC-powered AI solutions, serving as primary client contact, coordinating cross-functional teams, defining roadmaps, governing deployments, and ensuring production-ready, privacy-preserving inference across enterprise and government environments.

Lead design and operation of scalable ML infrastructure and pipelines, deploy and optimize models in production, integrate real-time and batch inference, mentor ML engineers, and drive MLOps best practices across teams while communicating solutions to stakeholders.

Design and maintain enterprise ontologies and knowledge graphs, integrate structured and unstructured data for LLM grounding, implement SHACL guardrails, build graph-to-vector retrieval (GraphRAG) and entity resolution pipelines, and collaborate with product, data, and engineering teams to operationalize agent-ready semantic layers.

Design, build, deploy and operate cloud-native, AI-enabled employee experiences using Python, AWS, Node.js and React. Contribute to architecture, platform components, CI/CD, DevSecOps, observability and AI-agent features while improving security, scalability and engineering quality.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Design and build an AI-native HR platform: backend systems (Python/Django/ASGI), integrate LLMs and agent frameworks, implement RAG/vector search, telephony/voice interfaces, and mentor junior developers while ensuring scalable, low-latency production deployments.

Design, build, and deploy LLM-powered solutions and scalable backend systems. Implement data extraction, semantic search, document analysis, agentic flows, and evaluation frameworks. Continuously evaluate, tune, and improve model performance while collaborating with product, design, and legal teams.

Post-sales, customer-facing role that drives developer adoption of AI tooling by designing and running hackathons, Customer Developer Days, and repeatable facilitation infrastructure. Partner with deployment and success teams to diagnose adoption stalls, run high-touch interventions, capture outcomes, and evangelize results. Must be technically credible with developers and able to troubleshoot and demonstrate AI coding tools in real time.

Senior individual contributor who architects, prototypes, and ships production-grade AI solutions for Finance. Focus areas include agentic AI, multi-agent orchestration, ML forecasting and anomaly detection, RAG and vector-store solutions, responsible AI and governance, and embedding AI into FP&A processes. Partners with engineering, IT, security, and finance teams to productionize systems and establish standards, monitoring, and controls.

As a Chatdesk Expert, you'll provide customer support through social media and email, ensuring great experiences, flexible schedules, and work from anywhere.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Design, implement, and optimize operational workflows across regions; identify inefficiencies and automate manual tasks using AI and automation tools; partner with Tech/Data and cross-functional teams to deploy scalable solutions; document playbooks; define and track KPIs to improve operational efficiency and performance.

Evaluate AI coding agents end-to-end, judging usefulness, high-level correctness, engineering judgment, and quality of explanations. Provide opinionated feedback, distinguish response quality levels, and help define standards for great AI-developer interactions. Participate in take-home evaluation and interview as part of a rolling, project-based engagement.

Design, build and deploy AI solutions (custom copilots, conversational agents, embedded AI) using Microsoft Copilot Studio and Azure AI Foundry. Integrate AI with Power Automate, Snowflake and enterprise systems, collaborate with stakeholders, operationalise models, monitor performance, and champion responsible AI practices.

Create AI-generated marketing assets and playable prototypes by combining visual artistry, generative models, and interactive prototyping. Produce video ads, cinematics, and interactive game creatives, curate datasets, fine-tune models, and collaborate with marketing, design, and engineering to maintain brand quality and improve generative workflows.

As an AI Product Manager, you will lead the application of AI in Health Information Systems, manage product portfolios, and ensure compliance with healthcare standards while delivering technology solutions that enhance patient care.

Lead strategy and delivery of AI-driven knowledge management for global service operations. Define product roadmap, data architecture and governance, identify automation opportunities, steer cross-functional agile squads, manage product lifecycle and stakeholder alignment, and drive global rollout and adoption.