Top Artificial Intelligence Jobs in Madrid

As a Senior AI Engineer, you will develop AI-powered features, integrating LLM APIs to enhance user workflows while collaborating with teams to ensure reliability and scalability.

The role involves delivering features across the CRM platform for distributors, using AI tools, handling fullstack development tasks, and collaborating closely with teams and customers.

Partner with Product, Design, Engineering, and Data teams to define KPIs and measurement for AI-powered features, guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and translate analysis into product recommendations and analytics best practices.

Build production-grade AI agent infrastructure for Fever’s Product Engineering organization, including agent harnesses, sub-agents, guardrails, evaluations, knowledge bases, cloud development environments, CI/CD delivery systems, observability, and measurement platforms. Optimize AI workflows for quality, speed, and cost while improving planning, coordination, and communication across engineering squads. Own backend systems end to end and establish engineering standards for reliable production software.

Design, build, and run production-grade agentic AI systems for enterprise clients: architect multi-LLM integrations, RAG pipelines, orchestration, LLMOps, and cloud-native deployments. Lead client engagements, set engineering standards, measure system metrics, and share reusable patterns to scale agentic solutions across verticals.

Build and deploy production-grade generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, backend services, data pipelines, evaluation harnesses, monitoring, guardrails, and cloud-native AWS infrastructure. The role owns model and agent reliability, cost, latency, drift detection, and release quality. It also involves architectural decisions, customer handover, documentation, mentoring engineers, and collaboration with forward-deployed executives and engineers.

Build and deploy production generative AI, RAG, and agentic systems for enterprise clients. Translate business workflows into technical solutions, develop evaluation harnesses, write production code, and deliver cloud-native systems on AWS. Lead architecture reviews, technical proposals, scoping, monitoring, cost optimization, and client handoff. Partner with senior stakeholders, own technical direction, mentor engineers, and help evolve reusable AI Blueprints across financial services, insurance, healthcare, and life sciences.

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

Develop and deploy production-ready generative AI applications using Python, LLMs, RAG, AWS Bedrock, and OpenAI APIs. Responsibilities include model customization, API integration, data preparation, performance tuning, production deployment, documentation, and cross-functional collaboration. The role focuses on applied engineering rather than research and requires experience integrating AI solutions into business applications.

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.

Build and improve Restream’s web interfaces and user-facing features for the AI Clips team. Design UI and business logic, collaborate with product designers, participate in code reviews, improve system reliability, and evaluate emerging technologies. The role requires strong JavaScript and TypeScript expertise, advanced React knowledge, CSS preprocessor experience, and familiarity with backend development, cloud infrastructure, containers, and continuous deployment.

Create high-volume, high-quality marketing content using generative AI and AI agents. Produce video, image, and social content, develop attention-driving campaigns, track AI and culture trends, and iterate quickly with the marketing and founding team.

Build features, prototypes, and demos using modern AI coding tools and agents; iterate prototypes into reliable, scalable production; direct and evaluate AI coding agents; collaborate closely with founders and senior engineers; explore and improve AI development workflows to accelerate product delivery.

Lead AI-native engineering: build and ship production-grade features, convert AI-assisted prototypes into reliable systems, own architecture/security/quality, direct AI coding agents, define engineering standards, and work closely with founders on product and technical strategy.

Design, build, evaluate, and deploy production-grade LLM and classical ML solutions. Create retrieval and agentic workflows, integrate AI into client apps, implement evaluation frameworks, optimize prompts and pipelines, and collaborate with product teams through discovery to production support.

Lead design and ownership of shared libraries, Spring Boot starters, and agentic frameworks. Set standards for agent orchestration, evaluations, and guardrails. Mentor teams on Python-based agents (LangChain/LangGraph), drive adoption, prototype research directions, and improve developer experience across Kotlin backend and client libraries.

Develop and maintain front-end and back-end AI-enabled web and desktop applications. Collaborate with cross-functional teams to design features, optimize scalability and security, troubleshoot defects, participate in code reviews, and implement AI assistant technology integrated with enterprise systems.

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.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

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.

Lead product development in the industrial sector focusing on IoT, edge computing, and AI applications. Oversee product lifecycle, conduct market research, and collaborate with cross-functional teams.

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.

As a Staff/Senior AI Product Engineer, you will build AI-powered product features, enhance the tech stack, and design AI workflows while collaborating with product teams. Your focus will be on agent-native architecture and improving the codebase for AI agents.

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.