Top Artificial Intelligence Jobs in Portugal

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.

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.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Lead and scale demand generation by building signal-driven, automated GTM systems using AI and no-code tools. Own MQL-to-pipeline metrics, optimize CAC, run performance campaigns, integrate CRM and enrichment workflows, collaborate with sales/RevOps to improve conversions and pipeline ROI.

Remote SDR responsible for outbound prospecting, qualifying leads, booking demos, maintaining CRM records, collaborating with marketing and sales, and meeting monthly KPIs in the AI SaaS space.

Lead development of scalable AI-powered GTM systems, migrating from no-code to Claude Code agent architectures. Build integrations, data pipelines, and reusable agent frameworks; ensure reliability, observability, and cost optimization. Hire, mentor, and manage a distributed engineering team while evaluating emerging AI tools and driving platform innovation.

Lead design, build, and scale client-facing and internal automation systems. Create advanced workflows (GoHighLevel, n8n/Make/Zapier), integrate platforms via APIs/webhooks, document SOPs, advise clients, maintain CRMs and troubleshoot live systems for reliability and scalability.

Design, develop, and deploy AI/ML models and LLM-powered workflows (chatbots, copilots, automation). Integrate models with APIs and SaaS platforms, fine-tune prompts, work with structured/unstructured data, monitor performance, and collaborate with product and engineering teams to deliver scalable AI solutions.

Build end-to-end AI-first features for a benefits platform, working across frontend (React/TypeScript) and backend (Python/FastAPI/SQLAlchemy). Ship production code, integrate ML components (LLMs, recommenders, RL), maintain CI/CD and cloud deployments (GCP preferred), collaborate with product/design, write tests and documentation, and participate in peer review and iterative development.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

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.

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.

Design, build, and maintain scalable Java microservices (Java 17/21, Spring Boot). Architect agentic AI systems using Spring AI or LangChain4j, integrate Azure Copilot via REST APIs, craft and version-control prompts, implement reliability guardrails and observability, and collaborate with product, frontend, and mobile teams to deliver end-to-end AI features.

Design, develop and deploy machine learning and deep learning (including genAI) models to improve credit risk data quality. Perform EDA, feature engineering, model evaluation, automation, and collaborate with stakeholders to translate business needs into data-driven solutions while ensuring regulatory compliance and coordinating monitoring committees.

Own and scale AI enablement: build core orchestration, LLMOps/MLOps platforms and governance, integrate generative AI into product, optimize cost/latency, run PoC-to-production delivery, upskill teams, standardize tooling and vendor strategy, and track adoption/ROI metrics.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

Design and implement generative AI and ML solutions, build multimodal agents and automated workflows, develop MLOps tooling and cloud deployment pipelines (Azure/AWS/GCP), use Python/Databricks/SQL/PySpark/Docker/LangGraph, document work and present results to stakeholders.