Top Artificial Intelligence Jobs in Portugal

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.

Own end-to-end feature delivery in an AI-first engineering team: direct and validate AI across the build loop, set quality standards for AI-generated code, develop prompts and agent workflows, mentor junior engineers, and collaborate with PM, design, DevOps and QA to ship production software.

Lead and grow a Lisbon-based engineering team building an AI-first product development lifecycle platform. Own roadmap, delivery, metrics, backlog prioritization, and adoption of AI tools and managed agents. Translate stakeholder needs, measure outcomes, and be accountable for engineering productivity and product impact.

The AI Lead Product Manager will define and own the product strategy for AI-powered health data, lead product teams, engage with stakeholders, and ensure compliance with regulations.

Design, build, and operate production-grade automations that reduce manual work across Operations, Support, Finance, and Tech. Identify high-volume automation opportunities, integrate with existing systems via APIs/webhooks, apply AI/LLMs for classification/routing/summarization, and ensure reliability, observability, and iterative improvement of workflows.

As an AI Engineer, you'll design AI tools for authors, enhance internal workflows, and build tools for AI development, collaborating with teams to identify AI opportunities.

The role focuses on enhancing AI adoption among engineering teams by delivering training, improving workflows, and developing enablement tools, while collaborating closely with various roles across the organization.

Create and edit short-form vertical videos for social media using AI tools, ensuring quality, character consistency, and appropriate syncing of audio elements.

Looking for Fullstack Developers to create an operating system-like platform for an AI agent. Candidates need strong development experience, OOP architecture knowledge, and Gen AI familiarity.

As a Curriculum Manager, you will manage the content development lifecycle, collaborate with instructors, and improve data science curriculum, ensuring technical accuracy and pedagogical effectiveness.

Develop, test and deploy LLM-based applications and AI agents for financial services. Build Python-based AI components, REST APIs, prompt chains, and scalable solutions while collaborating with cross-functional teams and following engineering standards.