Top Artificial Intelligence Jobs in Madrid

Develop and enhance a live drive-thru Voice AI product through software engineering, AI/LLM prototyping and fine-tuning, conversation analysis, system integrations, and customer requirements gathering. Build, test, deploy, and improve production features while evaluating real dialogs and collaborating with customers and internal stakeholders.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Build and enhance RAG systems, AI components, backend services, data pipelines, and REST APIs under senior guidance. Write production code, tests, evaluation harnesses, documentation, and runbooks. Support AWS deployments using containers and CI/CD, investigate model failures, and contribute to architecture and client handovers. The role offers increasing ownership of technical components and decisions while working in distributed, customer-facing delivery teams.

Design, implement, and optimize high-performance attention and foundational AI kernels for GPUs, NPUs, ASICs, and other accelerators. Build reusable attention-framework abstractions, profile workloads, develop benchmarks and correctness tests, analyze hardware and compiler behavior, and improve production inference performance. Collaborate with hardware and software teams, document designs, resolve ambiguous performance issues, and mentor engineers while supporting new accelerator architectures and AI workloads.

Leads an engineering track by empowering engineers, driving organizational and technical initiatives, improving collaboration with Product, and building a culture of ownership and continuous improvement. Responsibilities include managing ceremonies, hiring, onboarding, mentoring, performance management, career development, and scaling zero-to-one product initiatives. This is a people and organizational leadership role rather than a delivery management or hands-on technical authority position.

Lead Trade Nation’s data and AI strategy, with emphasis on deploying AI into business workflows and enabling measurable adoption. Own the data warehouse’s business-facing gold layer, semantic models, data quality, analytics engineering standards, and self-service analytics. Lead and develop data analysts while partnering with engineering, compliance, and senior stakeholders. The role requires financial services experience, strong SQL and data modelling skills, cloud data platform expertise, and the ability to deliver AI initiatives from concept through production.

Serve as the founding customer-facing engineer for Kimchi, leading product demonstrations, customer workspace setup, inference API integrations, model routing optimization, and governance policy implementation. Configure agent workflows, integrations, budgets, cost attribution, and access controls. Translate customer feedback into product improvements while identifying opportunities for revenue expansion. The role combines hands-on LLM and inference expertise with customer enablement and commercial thinking in a remote-first environment.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Serve as a technical-strategic partner for AI Decisioning customers, leading use-case delivery from discovery through go-live and expansion. Collaborate with engineers, executives, marketers, and IT stakeholders to define requirements, explore customer data, recommend marketing improvements, manage project risks, and develop delivery best practices. The role also contributes to AI Decisioning product strategy and requires customer-facing consulting, strong communication, and 10–25% travel.

Experiment with generative image and video models, build advanced ComfyUI workflows and automated pipelines, and create high-quality demos, examples, guides, and social content. Translate technical experimentation into engaging developer- and creator-facing material, rapidly evaluate new model releases, and collaborate with Growth, Product, and Engineering on launches and use cases.

Design and build production-grade agentic AI and ML solutions, including LLM and RAG systems, end-to-end data architectures, multi-agent workflows, guardrails, monitoring, and automated actions. Collaborate with engineering, project, and client teams to prioritize use cases, explain technical decisions, and deliver prototypes through production. Contribute to real-time data pipelines, AI evaluation, governance, and team knowledge sharing.

Develop and deploy AI-powered 3D graphics solutions for desktop, web, VR, and AR applications using the Evergine engine. Build computer vision and spatial computing models, including NeRF, Gaussian Splatting, point-cloud processing, SAMv2, and GroundingDINO or FlorenceV2. Design scalable algorithms, integrate machine learning frameworks, and collaborate with research and engineering teams in an Agile environment.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Leads research and production development of generative AI and large language model systems for cybersecurity. Responsibilities include defining AI research strategy, developing novel reasoning and summarization approaches, selecting model architectures and deployment strategies, building large-scale production systems, mentoring scientists, establishing evaluation standards, and guiding cross-functional technical initiatives. The role also represents the company through publications, speaking engagements, and technical thought leadership.

Lead customer deployments of Kubernetes and vCluster on bare-metal GPU infrastructure, configuring GPU nodes, networking, storage, RDMA, and InfiniBand. Validate production-ready managed Kubernetes environments, troubleshoot complex infrastructure, transfer operational knowledge, document reusable deployment playbooks, and provide feedback to Engineering and Product. Support pre-sales proof-of-value engagements and help AI cloud and factory customers independently operate and scale their platforms.

Leads Coverflex’s central AI Ops team, coordinating AI adoption and automation across departments. Owns prioritization, governance, reusable patterns, investment cases, spend and impact measurement. The role remains hands-on, building agents, skills and orchestrated workflows with business teams, while managing a small multidisciplinary team. It requires influencing stakeholders without direct authority, establishing safe operating practices, and demonstrating measurable returns from AI investments.

Build and own QuantPilot’s agent platform, including orchestration, tool loops, context management, sandboxes, model routing, product contracts, and production observability. Develop evaluation systems for agent quality, backtesting, regression detection, and statistical robustness. Establish market-data foundations, validate strategies, guide technical direction, refine product requirements, mentor engineers, and participate in on-call support.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, provider integration, LLMOps, observability, and reusable patterns. Embed with client teams, lead design sessions, tune embeddings/vector search, manage cost/latency tradeoffs, and define metrics for accuracy, safety, latency, and cost.

Manage multi-country payroll operations, compliance, audits, controls, vendor relationships, payroll analysis, and process improvements. Lead payroll system automation and scalability initiatives while ensuring accurate and timely processing across jurisdictions. Partner with finance, HR, treasury, and external providers, monitor regulatory changes, and mentor a global payroll team in a distributed technology environment.

Lead a small engineering team building and shipping AI-powered SaaS features. Set technical direction, establish standards for observability, testing, security, scalable architecture, and AI evaluation, while managing delivery timelines and team development. Remain hands-on by designing and implementing production AI systems using LLMs, embeddings, RAG, vector databases, and related tooling. Partner with Product and leadership on prioritization, architecture, resourcing, and risk management.

Build and operate Alan’s internal agentic AI platform, including web experiences, agent orchestration, secure connectors, isolated runtimes, automations, evaluations, and developer tooling. Own work end to end across React interfaces, APIs, infrastructure, reliability, security, and production operations. Collaborate closely with product teams and internal users to create trustworthy, inspectable AI workflows and scalable platform capabilities.

Build the Ops AI Platform from the ground up, developing reliable AI agents, agent tooling, internal tools integrations, evaluation systems, safety controls, and human-in-the-loop workflows. Scale automation from dozens to tens of thousands of daily tasks while influencing architecture, code quality, product direction, engineering culture, mentoring, and hiring.

Join Welo Data’s Hungarian contributor network for flexible, remote AI data projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, or chatbot responses; create and test prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Build and maintain PHP and Laravel backend services and APIs for a major product release. Own features from design through deployment and monitoring, integrate agentic AI workflows and LLM-based tooling, optimize performance and reliability, improve security, and mentor engineers. Collaborate independently with a cross-functional team on ambiguous technical challenges while delivering production-ready software.

Build and own production agentic AI runtime components, including context construction, retrieval and tool orchestration, ranking, scoring, guardrails, multilingual quality, latency and cost optimization, and evaluation systems. Diagnose production failures through traces and improve agent reliability for customer-facing commerce applications.