Top Artificial Intelligence Jobs in Portugal

Technical presales engineer who designs and demos cloud and AI/data architectures, runs workshops, builds or oversees PoCs, collaborates with sales and delivery teams, and translates customer needs into viable production solutions while balancing scope, budget, and technical debt.

As an AI Engineer, you'll design and build AI systems for production applications, focusing on client solutions and maintaining system performance through evaluations and observability.

Build and productionize AI-native systems for a fintech neobank: AI agents, LLM apps, RAG/retrieval, tool-calling, automations and AI-assisted experiences for onboarding, KYC, support, risk, CRM and operations while addressing accuracy, latency, cost, security and compliance.

Design and deliver cloud-based AI/ML systems with a focus on database architecture, scalable data pipelines, model training and production, generative and deep learning model customization, HPC algorithm R&D, data preprocessing, storage/retrieval design, and cross-functional infrastructure alignment including edge and HPC resources.

This role involves managing Microsoft Commercial Marketplace integrations, evaluating opportunities, conducting feasibility assessments, and leading R&D for novel API integrations and enhancements.

Define and lead implementation of scalable, secure AI/GenAI architectures (LLMs, RAG, vector DBs, agents). Set technical standards, build PoCs, integrate models with enterprise systems, ensure governance, and mentor engineering teams while supporting presales and client engagements.

Develop and maintain a multi-cloud GenAI platform: design, implement, test, deploy scalable APIs and backend services, build monitoring and KPIs, automate workflows, ensure security/compliance, and collaborate with international teams to continuously improve platform capabilities.

Design and build AI-driven conversational flows and integrations using LLMs, prompt engineering, REST APIs, and modern backend practices. Collaborate with multidisciplinary teams to influence product direction, write and refactor scalable code, and implement solutions in cloud environments (Azure/AWS).

Lead design and build Generative AI applications using LLMs and Azure AI services. Implement, deploy (Azure ML, CI/CD), integrate semantic search and embeddings, handle data engineering, and present solutions to stakeholders.

The role involves designing and implementing AI features within trading applications, focusing on model optimization, prompt engineering, and collaboration with teams to enhance software capabilities.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

The Senior Backend Developer will design AI pipelines, maintain backend microservices, manage AWS infrastructure, and ensure systems are production-ready while collaborating with cross-functional teams.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Lead strategic product roadmaps, conduct market intelligence, manage pricing strategy, apply data analysis, and mentor teams while collaborating with cross-functional partners.

Serve as a strategic partner to AI Agents product leadership to align customer insights, product priorities, and cross-functional initiatives. Drive operational excellence by optimizing existing product development programs, improving processes, enabling data-informed decisions, and producing executive-ready recommendations. Coordinate across Product, GTM, Data, and Customer Experience to improve discovery, planning, launches, and customer engagement while measuring impact of improvements.

Design, build, and ship customer-facing LLM features and agentic workflows end-to-end. Implement RAG systems (chunking, embeddings, retrieval, re-ranking), safe production deployment, evaluation pipelines, drift detection, and observability. Advise on retrieval architecture, drive Context Development Lifecycle practices, and embed as a hands-on individual contributor to raise squad-wide AI quality and adoption.

Author and iterate curriculum for experienced engineers transitioning to system architecture: validate syllabus, define learning objectives, produce AI-assisted lessons, design hands-on projects, write rubrics and reviewer guides, and collaborate with LX designers to refine content. Part-time contract (15–20 hrs/week) over ~4–6 months.

Lead live sessions, mentor senior engineers transitioning to architecture roles, review and assess projects, run architecture review boards, provide technical escalation and detailed feedback, record instructional content, and run mock interviews and office hours to ensure students meet program standards.

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.

Design and build production-grade multi-agent AI systems: orchestration, routing, memory, tool execution, multimodal workflows, and integrations with model providers and platform APIs. Implement testing, observability, and reliability practices; analyze and extend APIs and codebases to improve agent behavior, performance, and robustness in gaming and transaction-heavy domains.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

Build core AI platform capabilities (agent runtimes, model routing, tool registries) and production-grade services and tooling using Node.js, TypeScript, NestJS and React. Develop reusable AI infrastructure, work with LLMs and LangChain/LangGraph, and own features end-to-end to enable secure, scalable AI across the company.

Build and own AI/LLM-assisted product features in Ruby on Rails: integrate LLM APIs, implement prompts and retrieval patterns, ensure reliability, security, and performance, write automated tests, debug production issues, and improve system behavior over time.