Top Artificial Intelligence Jobs in Madrid

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Design, build, and deploy production-grade agentic AI systems at enterprise scale. Lead client engineering engagements, define RAG and multi-LLM standards, own LLMOps and eval harnesses, establish observability, safety, and cost controls, and publish reusable patterns and metrics to demonstrate business impact.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

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 polished, user-centric mobile and web interfaces for BJAK's AI-driven financial products. Create wireframes, flows, prototypes and high-fidelity UI, evolve the design system, collaborate with product and engineering, iterate from user feedback and analytics, and ensure accessibility, responsiveness and visual consistency across devices.

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

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.

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

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.

Own the end-to-end customer-facing conversational agent across chat and voice. Lead roadmap, conversation and interface design, partner discovery and onboarding, prototyping, quality metrics, and agent behavior specification. Work hands-on in Figma, build prototypes with AI coding tools, and iterate from production data with cross-functional GTM and success teams.

Build end-to-end AI-native product features across frontend, backend, and LLM integrations. Design and deploy agent workflows, streaming real-time interactions, memory, and tool integrations. Improve reliability, observability, fallbacks, and reduce latency while collaborating with ML and product teams to ship production-grade, multi-step workflow capabilities.

Build and ship end-to-end AI features: design prompts, tools, memory, and agent workflows; turn model outputs into reliable behavior; debug across model, orchestration, infra, and UX; optimize latency, cost, and reliability; develop evaluation frameworks and collaborate with product and engineering to deliver robust ML-powered features.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

The AI Developer will design, implement, and deploy AI agents and workflows, integrating advanced models and ensuring reliable performance in production environments.

Own product capabilities that improve brand visibility across search engines and LLMs. Evaluate visibility signals (backlinks, mentions, PR, communities), design agentic AI workflows, advise on off-page and technical SEO, analyze review ecosystems and media channels, prototype AI-agent solutions, and drive customer adoption with cross-functional teams.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

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.

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.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

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.

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.