Top Artificial Intelligence Jobs in Madrid

Own the lifecycle of AI products by translating business priorities into executable roadmaps, partnering with engineering and design, driving delivery, and incorporating user feedback. Lead client demonstrations, define and evaluate product metrics, coordinate cross-functional teams, and align product development with broader innovation strategy. The role requires strong product management, leadership, communication, analytical, agile, and user research skills.

Build Solidgate’s ML/AI function from the ground up, developing real-time payment routing models and production pipelines. Own the full ML lifecycle from experimentation and A/B testing through deployment, while solving modeling challenges involving large-scale transactional data. Partner with Product, Engineering, and Data Platform teams to deliver measurable improvements in conversion, approval rates, losses, and revenue.

Leads OKX’s global marketing compliance function, building policies, controls, review and approval processes, monitoring programs, and regulatory change management for advertising, promotions, social media, influencers, affiliates, and product launches across CeFi and DeFi. Partners with Marketing, Product, Legal, Risk, and Compliance teams; manages incidents, supports audits, delivers training, and scales the compliance team and framework.

Leads research and advisory work for CIOs on data, analytics, and AI strategy, governance, operating models, architecture, value realization, and emerging technologies. Develops thought leadership, market analysis, presentations, and actionable client guidance; evaluates agentic and composite AI, modern data platforms, AI-ready data, organizational maturity, and capability-building needs. Represents Gartner at client meetings and conferences, supports sales, mentors colleagues, and shapes research agendas.

Lead the engineering function on a remote consulting mission by setting technical direction, managing and mentoring engineers, driving the roadmap, overseeing complex delivery, improving engineering practices, and partnering with product and business stakeholders. The role requires hands-on technical leadership, architectural judgment, agile expertise, and evaluation of emerging technologies including cloud, AI, and blockchain.

Lead the engineering function for a growing technology company on a renewable consulting mission. Responsibilities include setting the engineering roadmap, managing and mentoring engineers, guiding architecture and technical decisions, leading complex projects, improving engineering practices, and collaborating with product and business stakeholders. The role requires hands-on technical involvement, strong software engineering expertise, and the ability to evaluate emerging technologies including cloud, AI, and blockchain. French fluency and strong English communication skills are mandatory.

Lead enterprise Agentforce architecture and hands-on agentic AI engineering for European clients. Design agent workflows, configure Salesforce agents, integrate deterministic Apex and Flow logic, implement LLM orchestration, RAG, APIs, MuleSoft pipelines, security guardrails, and automated testing. Conduct technical discovery, advise enterprise stakeholders, and guide engineering teams through production deployments. This is a fractional, project-based advisory role requiring deep Salesforce architecture and advanced LLM engineering expertise.

Lead Nuitee’s agentic AI strategy and capabilities for searching, booking, paying for, modifying, and canceling hotel stays. The role combines hands-on software engineering, product development, technical leadership, commercial opportunity identification, and team building. Reporting to the CEO, this leader will design secure agent-native APIs and MCP tools, improve transaction reliability, and take new products from concept to production across Nuitee’s travel infrastructure.

Build a model enablement platform for heterogeneous AI computing across AIPUs, CPUs, GPUs, NPUs, and other accelerators. Responsibilities include model ingestion, graph partitioning, compilation, runtime execution, performance profiling, memory and scheduling optimization, large-model offloading, and verification integrated into CI. The role combines architecture ownership with hands-on C++ and Python engineering across model frontends, compilers, runtimes, and application pipelines.

Own LLM and VLM enablement on Axelera AI hardware, from studying and profiling models through productionizing them in the Voyager SDK. Responsibilities include PyTorch references, quantization, weight mapping, model contracts, pipeline integration, KV-cache management, multimodal serving, accuracy validation, and bring-up platform development. The role also shapes GenAI, agent, and speech capabilities for edge devices.

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.

Direct AI micro-drama production from script review through final episode approval. Responsibilities include evaluating storylines, emotional impact, hooks, and cliffhangers; directing AI video creators on cinematic execution; reviewing edits; maintaining visual consistency; improving production workflows; and using audience retention data to guide future episodes.

Lead the AI architecture for Productboard Insights, designing agent-native systems, context infrastructure, self-healing workflows, evaluation processes, and AI code-review optimization. Own technical direction across Insights, Spark, and platform teams; drive features from concept through beta; optimize quality, latency, cost, and reliability; write production code; and develop engineers through documentation and reviews.

Design, build, and deploy production-grade generative AI assistants and agents using LLMs, RAG, embeddings, vector databases, tool calling, and orchestration. Develop full-stack applications with Python, TypeScript, React, and AWS services including Bedrock and AgentCore. Implement infrastructure as code with Pulumi, integrations through APIs and MCP, security controls, observability, automated evaluation, and performance optimization. Communicate technical decisions to business stakeholders and continuously assess emerging AI technologies.

Design, build, and deploy production-grade generative AI assistants and agents using LLMs, RAG, tool calling, memory, and orchestration. Develop full-stack solutions with Python, TypeScript, React, and AWS services including Bedrock and AgentCore. Build integrations, APIs, authentication, infrastructure as code, observability, evaluation, and cost and latency optimization. Communicate technical decisions to business stakeholders and proactively adapt to emerging AI technologies.

Build AI-powered automation agents and modernize legacy enterprise systems for SME clients. Responsibilities include developing Python backends, integrating Salesforce, SAP, and Microsoft Dynamics, cleaning and transforming messy data, and delivering outputs through Power BI, PDFs, and APIs. The role involves working with LLMs, agent frameworks, and AI coding tools while collaborating with Spanish- and English-speaking clients in a fully remote consultancy environment.

Build and own AI models, data pipelines, evaluation suites, and AI product features for construction procurement software. Develop document extraction and workflow automation using generative AI, LLMs, RAG, and optimization algorithms. Design complex data models, integrations, backend services, and production systems using Go, GraphQL, PostgreSQL, and AWS.

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.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Owns end-to-end product and operational analytics for AI-powered and conventional automation features. Responsibilities include experiment design, funnel and adoption analysis, AI output evaluation, hallucination and failure investigation, monitoring, dashboards, and measuring business impact. The role partners with Product, Engineering, Clinical, MedOps, and Legal, using SQL and Python to analyze messy production data and translate findings into decisions.

Build and operate production AI services, including multi-agent systems, LLM pipelines, MCP servers, RAG and document-ingestion workflows. Develop reliable Python backend services, evaluate model quality, manage deployments, observability, authentication and privacy safeguards. Collaborate with engineering and product teams while supporting AI governance, risk documentation and ISO evidence. The role focuses on practical business automation across document processing, customer support, CRM and account operations.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Build BLP’s core software across infrastructure, backend, machine learning, and product extension platforms. Responsibilities include operating secure and observable cloud infrastructure, developing performant distributed systems, applying AI to business problems, and enabling customer process automation. The role emphasizes formal reasoning, correctness by construction, maintainable systems, type systems, and functional programming. Work is available onsite in Zurich or remotely within Europe.

Build and operate production AI infrastructure powering intelligent agents at scale. Develop high-performance Rust services for model inference, orchestration, and execution; design scalable distributed systems; establish MLOps, observability, guardrails, and failure recovery; and optimize latency, throughput, reliability, and cost. Partner with agent systems and infrastructure teams to turn prototypes into hardened services serving millions of users.

Serve as the senior technical expert for EMEA AI organizations training and post-training large-scale foundation models. Advise customers on distributed training infrastructure, PyTorch, Megatron-LM, NeMo, reinforcement learning, GPU utilization, communication overlap, and memory management. Build strategic technical relationships, translate customer needs into NVIDIA product roadmap input, collaborate with research and product teams, and support hackathons, demonstrations, and technical conferences.