Top Artificial Intelligence Jobs in Portugal

Design, build, and operate backend services for an AI-driven financial assistant. Own full SDLC, ensure performance, scale, resilience, and collaborate in small autonomous teams.

Build and ship native iOS features in Swift for an AI-powered neobank, integrating backend APIs, improving performance and reliability, creating clean UX flows for financial services, and using analytics to iterate. Implement AI-assisted mobile experiences where beneficial.

Build and ship native Android features in Kotlin and Jetpack Compose for an AI-enabled neobank. Integrate backend APIs, improve performance, stability, and UX, use analytics and production data to iterate, and implement on-device AI with TensorFlow Lite where it improves user journeys.

Build and ship Kotlin-based native Android features for an AI neobank: implement payment, insurance and financial flows; integrate REST APIs; improve performance, reliability, and on-device AI using TensorFlow Lite; collaborate with product and design to simplify user journeys and use analytics to iterate.

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.

Lead full‑stack web application development while integrating AI-assisted workflows. Use AI coding assistants and agentic techniques to design, implement, test, and validate production‑grade frontend and backend systems. Build reusable prompts and agent workflows, establish verification and guardrails for AI-generated outputs, contribute to architecture and engineering standards, and mentor teams on responsible AI-enabled development.

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.

Build and own reusable accelerator core and multi-agent workflows, design agent behaviors (coding, planning, testing), implement evaluation pipelines and telemetry, and ensure reliability, cost control, and observability for production agentic systems.

Lead AI adoption for business teams: run assessments, prioritize use cases, design and deliver workshops, activate champions, remove blockers, track adoption KPIs, and hand teams off as self-sufficient while routing engineering needs to technical teams.

Own the AI platform roadmap and build reusable capabilities (prompting, RAG, tool calling, agents, evaluation, observability). Run discovery, prioritize AI investments, partner with Product, Engineering, Design and Data, guide other PMs, define success metrics, and ensure scalable, reliable AI-powered features that deliver customer and business value.

Design and execute integration test plans, validate AI components, collaborate with developers, and mentor junior QA engineers, focusing on AI-driven testing methodologies.

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.

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.

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

Design, build and operate cloud-native full‑stack web applications for a healthcare SaaS. Integrate AI-native features using LLMs, retrieval, agents and workflows. Own reliability, security and DevOps practices; collaborate with product, UX and architects; stay close to clinical users to deliver outcome-driven features.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.

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.

Lead architecture and productionization of NLP, voice, and ML systems; mentor engineers; integrate AI features with backend services on AWS; build evaluation, monitoring, and responsible-AI safeguards; drive AI strategy and technical vision.

Design and implement AWS-based data platform components, build multi-agent automated ETL/ELT pipelines, enable LLM-augmented capabilities (RAG, semantic search), collaborate with engineers, analysts and data scientists, ensure data quality, security, and production readiness.

Support design, development, testing, and deployment of AI solutions within cross-functional Agile squads. Implement ML, Generative AI, LLM and RAG capabilities; collaborate on data integration and feature engineering; follow MLOps/DevOps practices; participate in model evaluation, monitoring, and optimization; ensure security, privacy, and responsible AI; contribute to documentation, code reviews, and continuous learning.

Lead technical delivery and product ownership for enterprise AI solutions: define strategy, translate requirements, lead AI squads across ML, data engineering, and software teams, enforce MLOps and governance, and drive deployment, adoption, and measurable business outcomes.

Lead the design, build, and scaling of cloud-native AI data platforms and pipelines. Act as Technical Product Owner and squad lead to deliver governed data assets, MLOps, feature stores, vector DBs, and CI/CD to operationalize Generative AI, LLMs, RAG, and analytics solutions while ensuring security, compliance, and platform adoption.

Lead and build the AI foundation for an AI-native vulnerability management platform. Own AI strategy, evaluation frameworks for non-deterministic agents, fine-tuning and model-routing experiments, prototype and ship agentic features, and lead a small hands-on AI team to productionize breakthroughs in generative AI for cybersecurity.