Top Artificial Intelligence Jobs in Portugal

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

Design, build and deploy AI solutions (custom copilots, conversational agents, embedded AI) using Microsoft Copilot Studio and Azure AI Foundry. Integrate AI with Power Automate, Snowflake and enterprise systems, collaborate with stakeholders, operationalise models, monitor performance, and champion responsible AI practices.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Work with the Platform Owner to translate go-to-market needs into AI-augmented internal tools: gather requirements, write functional and technical specs, define acceptance criteria, coordinate AI-assisted implementation, run automated quality checks, enforce architecture and design standards, and keep system documentation current while collaborating across US and EMEA stakeholders.

Lead technical direction for Sovos's AI Platform and LLMOps practice: set architecture, guardrails, observability, model and prompt strategies, enable teams through workshops and tooling, and mentor engineers to scale agentic and LLM-based systems into production.

Design and build production-grade AI applications and services in Python, integrating LLMs with backend APIs and enterprise systems, implementing evaluation frameworks, observability, CI/CD, and reusable components to ensure reliable, testable, and scalable AI features.

Participate in a 14-day notification-sorting pilot using a qualifying Google Pixel device. Interact naturally with apps that generate notifications, complete daily sorting tasks and short feedback/check-in forms, and report technical issues. Must follow instructions, maintain device as primary phone during the project, and provide timely, high-quality feedback to improve mobile AI notification management.

Build and ship end-to-end AI features: design prompts, tools, memory, and agent workflows; convert model outputs into reliable behaviors; debug model, infra, and UX issues; optimize latency, cost, and reliability; develop evaluation frameworks and collaborate with product and engineering to deliver production ML systems.

Build end-to-end AI-native product features across frontend, backend, and model integrations. Design and deploy agent workflows, integrate LLMs, memory, and external tools, improve real-time streaming, latency, reliability, observability, and fallback mechanisms, and iterate on failures to deliver dependable, multi-step task completion in production.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Lead integration and automation of the DevOps platform (VCS, CI, artifact registry, AI Code Assistant) to improve developer experience. Build platform features with Python/Django, implement governance-as-code, automate SSDLC workflows, engage internal stakeholders, and standardize blueprints and patterns for ~1,500 developers.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Design, build and deploy AI-powered solutions (custom copilots, conversational agents, embedded AI) using Microsoft Copilot Studio, Power Automate and Azure AI Foundry. Integrate with Snowflake and enterprise systems, collaborate with stakeholders to define use cases, operationalise models, maintain reliability and governance, and evaluate emerging AI tools.

Lead the development of computer vision systems for scene understanding, manage technologies, mentor engineers, and stay updated on AI trends.

As an AI Transformation Consultant, you will bridge business strategy and technical execution in AI initiatives, leading workshops, defining governance models, and ensuring alignment with regulatory requirements.

As part of the Advanced Analytics and AI team, you will develop and deploy machine learning models, engage with business functions to create AI opportunities, and support AI capability roadmaps, all while working in a collaborative international environment.

Lead architecture and build an automation and AI-native toolchain (fusing traditional automation with agentic workflows), mentor teams, drive cross-department adoption, standardize practices, and evolve IT operations toward infrastructure-as-code and platform-first models.

Design and scale GTM systems and AI-powered workflows across sales, marketing, and product. Build agents, integrate Salesforce/HubSpot, turn product and user data into growth signals, create lead-scoring/routing/onboarding flows, identify bottlenecks, run experiments with founders to drive measurable revenue impact.

Lead eCommerce and AI initiatives to improve customer journeys and frontend experiences. Translate business needs into technical solutions, drive chatbot and conversational commerce projects, evaluate AI opportunities, collaborate with architects and product teams, and manage small-to-medium IT projects from concept to rollout.

Lead research and engineering for large-scale LLM and multi-modal pre-training: design architectures and tokenizers, curate massive datasets, run distributed training on thousands of GPUs, debug performance bottlenecks, and improve cross-modal alignment and efficiency.