Top Artificial Intelligence Jobs in Madrid

Build and operate the internal AI platform used by 600+ developers: secure AI coding environments, AI-assisted CI and agentic AWS workflows. Own products end-to-end, improve developer experience, ensure reliability/observability/cost-awareness, participate in on-call, and capture reusable patterns and templates for broad adoption.

Lead the AI function, define roadmap, and build production generative AI systems (voice, chat, document). Split time between hands-on engineering (~40%) and leading the AI team, establishing RAG/agentic workflows, evaluation, observability, and best practices. Collaborate with product and engineering to deliver reliable, user-focused AI features.

Lead architecture and implementation of a reproducible, control-first generative AI media production platform. Build and deploy modular pipelines (node-based and code-first), train LoRAs, integrate ControlNet/IP-Adapter/inpainting, implement automated QC against product references, and enable non-technical teams with one-click tools and presets. Own technical roadmap and collaborate with design and creative teams.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

The role involves architecting AI solutions for compliance, ensuring regulatory adherence, automating processes, and enhancing team capabilities through technology and training.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead end-to-end technical projects for a Voice AI CX platform: scope, plan, manage releases, align product to customer requirements, track metrics, ensure quality, and communicate status to clients and engineering.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Build and own AI-driven marketing workflows and HubSpot CRM operations. Design, deploy, and document automations (content drafting, repurposing, lead scoring, AI SDRs), integrate AI tools, keep data clean, and deliver dashboards and reports supporting sales and marketing.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Lead design and implementation of CI/CD and AI-related product features, owning end-to-end delivery. Build and secure CI/CD systems, work with containers, registries, and Kubernetes, extend agentic AI into customer experiences, guide architecture, and collaborate closely with Product and front-end teams.

Build and own end-to-end features for Chainguard's AI-enabled CI/CD and supply-chain security products. Design, implement, and iterate on integrations like hardened GitHub Actions, container tooling, and agentic AI features. Drive architectural decisions, collaborate with Product and front-end teams, and ship secure, production-ready developer tools.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

Own lifecycle marketing campaigns across email, push and in-app channels: write copy, build automated customer journeys and behavioral triggers, run A/B tests, analyze performance with product analytics, and work with Product and Tech to optimize activation, retention and monetization.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Build and own end-to-end data foundations, including ingestion pipelines, dbt models, data marts, semantic layers, and AI-ready datasets. Integrate fragmented marketing, advertising, ecommerce, analytics, and CRM data across a multi-tenant Snowflake environment. Manage platform API changes, data quality, restatements, performance, and client-specific modeling. Collaborate with product, engineering, AI, BI, and client teams while using AI coding agents to accelerate reliable production development.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Lead integration of LLMs and generative AI into production applications, implement RAG and prompt-engineering solutions, build CI/CD pipelines, collaborate with cross-functional teams, mentor engineers, and drive AI automation and platform improvements.

Partner with government customers and partners to design, prototype, and deploy AI solutions (computer vision, LLMs, generative/agentic AI). Create technical collateral, lead hands-on trials and deployments using NVIDIA hardware/software, and advocate product requirements across product, sales, and engineering teams.

Lead product direction and execution for major areas of an AI Neobank app. Define roadmaps, priorities, standards and metrics across squads; guide PMs, designers and engineers; translate regulatory and operational constraints into product decisions; drive launch quality, post-launch learning and continuous improvement across payments, savings, investing, insurance and travel features.

Own product areas in BJAK's AI neobank app: define problems, scope, metrics, roadmaps; work with engineering, design, data, compliance; launch features, monitor performance, iterate, and balance speed, UX, business impact, and operational complexity.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.