Top Artificial Intelligence Jobs in Seville

Own the architecture and technical direction of the company’s AI platform, including model gateways, routing, agent orchestration, MCP, identity and governance, evaluation, observability, security, and FinOps. Build and operate production services, establish engineering standards and golden paths, partner with Security and Legal, mentor engineers, influence cross-functional decisions, and participate in on-call and incident response.

Build and operate production AI products using generative AI, LLMs, RAG, vector search, and agentic systems. Develop scalable machine learning services, pipelines, APIs, infrastructure, and automated evaluation systems. Improve AI quality, reliability, security, performance, and cost while establishing engineering standards. Collaborate with Product, Engineering, Data Science, Data Engineering, and Analytics teams to deliver dependable customer experiences.

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.

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.

Build and deploy reliable, measurable, and safe AI agents for real business workflows. Work within Marketing Engineering and growth pods to identify problems, develop end-to-end solutions, evaluate LLM performance, and measure business impact. The role involves productionizing agent systems beyond demos, using Python, SQL, LLM APIs, and orchestration tools, with potential integration into go-to-market data and marketing systems.

Conduct research, market sizing, forecasting, competitive analysis, and thought leadership on enterprise AI services. Develop actionable insights for technology providers and senior executives through published research, presentations, conferences, and client engagements. Assess market trends, demand drivers, customer behavior, and growth opportunities while advising clients, supporting sales, reviewing content, mentoring analysts, and improving research processes.

Leads end-to-end architecture for enterprise data, analytics, and AI solutions in a Microsoft-first environment. Translates business requirements into secure, scalable designs and guides prototyping, implementation, architecture reviews, troubleshooting, governance, and production readiness. Requires deep hands-on experience with Azure data platforms, Power BI, enterprise security, Azure OpenAI, and retrieval-augmented generation patterns.

Lead the strategy and roadmap for Wrike’s AI Platform and Ecosystem, including MCP, Skills, and GraphQL. Direct the core MCP team, establish standards for agent-facing tools, guide GraphQL product evolution, and manage plugin distribution across AI marketplaces. Partner with engineering, architecture, security, customers, and product teams to improve reliability, governance, permissions, documentation, developer experience, and agent workflows. Use customer research, adoption data, experiments, and ecosystem feedback to prioritize outcomes.

Manage influencer campaigns end to end across multiple brands and markets. Responsibilities include creator casting, briefing, negotiation, contracting, campaign timelines, budgets, approvals, client communications, performance tracking, reporting, and optimization. The role requires using CPV and campaign KPIs to manage commercial outcomes, ensuring content meets brand and compliance requirements, and operating as a power user of Kyra’s platform and AI tools.