Top Artificial Intelligence Jobs in Spain

Develop, train and deploy multimodal AI models for biomedical diagnostics and clinical research. Prepare and clean data and ground truth, evaluate algorithms, collaborate with clinicians and product managers, deploy models in real-time with engineering, draft technical documentation, and lead/participate in scientific publications from experiment design to submission and peer review.

Lead design and delivery of scalable, cloud-native AI features across front-end and back-end. Architect APIs and services, modernize legacy systems, champion DevOps/observability, mentor engineers, own reliability, and collaborate cross-functionally to deliver production-ready, secure software.

Lead communications, engagement and programme coordination for an AI-first transformation. Curate AI Academy content, develop internal AI communications, drive employee adoption, maintain programme plans, track risks and milestones, prepare leadership materials, and coordinate stakeholders across global teams to increase AI confidence and use.

Lead a cross-functional squad to define product vision, roadmap and deliver new features for institutional English assessments. Translate business needs into requirements, write and prioritize epics and user stories, run agile ceremonies, collaborate with UX/development/marketing/operations, use data to iterate, and mentor colleagues to achieve measurable business outcomes.

The Project Manager will lead software integration projects in the life sciences sector, managing resources, tracking progress, and ensuring timely delivery while collaborating with various stakeholders to improve platform solutions.

The Senior Data & AI Strategist will lead data and AI transformation initiatives, advise clients, and ensure impactful strategy delivery, while mentoring team members.

Lead business development in Life Sciences, drive client acquisition, and develop AI & Data strategies. Mentor teams and foster collaborations across departments.

The GenAI Engineer will develop AI Agents for Board's platform, focusing on architecture design, prompt engineering, and performance evaluation.

Lead research and apply state-of-the-art ML (NLP, IR, recommender systems, generative AI) to large-scale business problems. Design experiments, develop scalable ML solutions, collaborate with engineering teams, mentor scientists, and communicate research to stakeholders while driving adoption and publishing results.

Lead end-to-end AI transformation: map workflows, prioritize use cases by ROI, build prototypes to production, establish governance, drive adoption, and measure impact across business functions.

Operate and improve the Guardicore Data & AI Platform for reliability, availability, performance, and security. Manage Kubernetes infrastructure, observability, CI/CD, and IaC; lead production investigations, automate remediation with AI/LLMs, partner across engineering teams, and participate in on-call rotations.

Own reliability, availability, performance, and operational readiness of a cloud-native data and AI platform. Operate Kubernetes infrastructure, improve observability, security, performance and cost efficiency, lead production investigations, use AI/LLMs for automation, collaborate across DevOps, Software, Data and Security teams, and participate in on-call rotations.

Design and implement AI-driven SDLC tools and MCP-based, context-aware developer workflows. Integrate LLMs, RAG, embeddings and vector DBs into CI/CD, test automation, codegen, PR analysis, and developer tooling. Collaborate with engineering, platform and product teams to scale AI adoption.

Design, build, and operate production-grade LLM-powered backend services and APIs. Implement prompt engineering, tool-calling, safety guardrails, observability, testing, and continuous model and operational improvements to deliver reliable agentic commerce solutions.

Lead design and delivery of agentic AI and LLM platforms across commerce systems. Define production-ready agentic patterns, drive AI-powered SDLC, build reusable capability layers, own context engineering (grounding, retrieval, memory), establish observability, governance and guardrails, collaborate cross-functionally, and act as technical lead for complex initiatives.

Lead operations for large-scale AI infrastructure: troubleshoot Linux, Kubernetes, GPU, networking, and storage incidents; drive reliability, root-cause analysis, automation, observability, and operational standards; mentor engineers and collaborate with vendors and datacenter teams.

Hands-on platform engineering role building and operating an internal Agentic AI platform: implement infrastructure-as-code, security and secrets management, GitOps delivery, observability, autoscaling, API/AI gateway routing, and run day-to-day operations including incident response, backups, and runbooks.

Lead and deliver a portfolio of AI, automation, and innovation projects (POCs, MVPs, pilots) across Business Assurance IT. Manage cross-functional teams, develop project plans, business cases, budgets, timelines and success metrics, coordinate governance and executive reporting, and drive scaling of successful AI solutions.

Lead engineering enablement across eight companies to cascade CTO AI vision into team discipline. Detect and package successful practices, run cross-company diffusion, operate cadence of validation and demos, measure adoption and outcomes, triage deeper needs to PoCs/workshops, and capture reusable artifacts and metrics.

Design, build, and operate the AI storage layer for GPU-heavy edge and core deployments. Architect and optimize storage for distributed training and inference, integrate storage with Kubernetes (CSI), tune performance (RDMA, NVMe-oF, GPU Direct Storage), lead reliability and operational practices, and provide staff-level architectural ownership and cross-team influence while executing hands-on storage engineering.

Own design, deployment, and operation of high-performance AI GPU fabrics and datacenter networking (RoCE, RDMA, BGP/ECMP, EVPN/VXLAN). Provide staff-level architecture, cross-team technical leadership, incident escalation, automation (Python/Bash), and operational standards for global datacenter and edge deployments, including inter-datacenter transport and security.

Build and maintain production LLM systems: design agentic multi-step workflows (LangChain/LangGraph), manage RAG pipelines (parsing, embeddings, vector DBs), develop and version prompts, implement evaluation frameworks for hallucination and grounding, and apply CI/CD, testing, logging, and observability. Work closely with AI and engineering leadership in a hybrid Barcelona role requiring English and Spanish fluency.

Operate and support large-scale AI infrastructure: monitor production GPU-backed Kubernetes platforms, troubleshoot infrastructure, networking and hardware incidents, participate in incident response and RCA, improve observability and automation, and maintain runbooks and operational documentation.

Lead and resolve complex incidents across large-scale NVIDIA GPU, Kubernetes, networking, storage, and hardware environments. Drive platform reliability, observability, automation, and operational standards. Mentor engineers, perform root cause analyses, collaborate with vendors and datacenter teams, and support adoption of AI-powered operational services.

Provide research and advisory to global CFOs and Controllers on transforming controllership processes (AP, AR, financial close) using AI, automation and finance technologies; produce published research, vendor evaluations, and client-facing presentations; collaborate with analysts and deliver strategic, actionable recommendations to improve financial reporting and automation outcomes.