Top Artificial Intelligence Jobs in Madrid

The VP Field Marketing is responsible for leading marketing strategy and execution in Europe, focusing on demand generation, brand awareness, and pipeline development while collaborating cross-functionally and driving a data-driven culture.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

Define and enforce enterprise architecture for Migrant Solutions. Design and implement software landscapes, APIs and web-based applications. Create roadmaps, plans, POCs, and technical guidance. Track risks and ensure solutions align with organizational requirements and enterprise architecture.

The Chief Data & AI Officer will lead the establishment of a company's data and AI infrastructure, ensuring reliable information capture, decision-making processes, AI system integration, and compliance with governance standards.

Lead global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

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 Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Support Spain (and other markets) by building consistent KPIs and AI-accelerated reports, analysing portfolio and market data to improve NOI and operational performance, driving AI adoption, supporting reporting, testing, systems optimisation, due diligence, and cross-functional data initiatives to inform decisions and process improvements.

Work with traders, asset operators, and developers to analyze electricity market outcomes, improve trading and operational strategies, and drive customer success. Translate market workflows into product requirements, collaborate with engineering to enhance analytics, dashboards, and optimization models, and lead customer onboarding, implementation, and ongoing technical account management.

Own end-to-end delivery of AI-powered automations: scope and shape client solutions, build demos and production systems (Python, FastAPI/Gradio, serverless), design prompts and data pipelines, validate outputs, and onboard clients while contributing patterns and R&D to the practice.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

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.

Remote SDR responsible for outbound prospecting, qualifying leads, booking demos, maintaining CRM records, collaborating with marketing and sales, and meeting monthly KPIs in the AI SaaS space.

Design, build, and optimize AI-driven automation workflows using platforms like Zapier/Make and LLMs. Integrate APIs, develop Python/JavaScript backend logic, maintain automation pipelines for sales/marketing/support, monitor systems, troubleshoot issues, and document scalable SOPs.

Design, develop, and deploy AI/ML models and LLM-powered workflows (chatbots, copilots, automation). Integrate models with APIs and SaaS platforms, fine-tune prompts, work with structured/unstructured data, monitor performance, and collaborate with product and engineering teams to deliver scalable AI solutions.

As an AI Engineer, you'll design AI tools for authors, enhance internal workflows, and build tools for AI development, collaborating with teams to identify AI opportunities.

Build reusable AI developer tools, RAG knowledge systems, and AI-assisted SDLC workflows. Integrate coding assistants and design-to-code tooling, define secure observable patterns, and own reliability, usage, and cost for internal AI platforms.

The Senior AI & Computer Vision Software Engineer will work on innovative software solutions, bridge research and production, and mentor others while working in an Agile environment.

Design, prototype and deliver production-ready AI solutions for insurance, focusing on agentic AI, multi-agent systems, LLMs, RAG pipelines and computer vision. Research and implement state-of-the-art models, build scalable products, take solutions from architecture and PoC to production, and collaborate with data science and engineering teams.

Own and scale AI enablement: build core orchestration, LLMOps/MLOps platforms and governance, integrate generative AI into product, optimize cost/latency, run PoC-to-production delivery, upskill teams, standardize tooling and vendor strategy, and track adoption/ROI metrics.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.