Top Artificial Intelligence Jobs in Portugal

Deploy and integrate ICEYEs intelligence platform into secure, often air-gapped environments. Build containerized applications, data pipelines, RAG systems, and AI agents (LangChain/LangGraph). Fuse SAR and multi-source intelligence, prototype mission-focused tools, train analysts, and iterate with customer feedback. Travel ~40-60% and obtain high-level national/NATO security clearances.

Lead prompt engineering initiatives, coach and support business users on AI tools, create best practices and training, evaluate AI platforms, mentor junior staff, and document technical learnings to integrate AI into workflows.

Design and build scalable, accessible frontend applications for AI-driven Contact Center and UC products. Implement responsive interfaces with Angular/React, integrate with backend AI services (REST/GraphQL), optimize performance, participate in architecture and code reviews, and collaborate with cross-functional teams in an Agile environment.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Design, build, and run production agentic workflows and reusable components for delivery processes. Implement RAG-backed knowledge bases, monitoring, evals, observability, and human-in-the-loop feedback to improve agents and integrate them with delivery teams and business tools.

Lead end-to-end implementation of QVAC-powered AI solutions, managing client engagement, cross-functional coordination, deployment roadmaps, governance, and production rollouts. Translate business needs into technical deployment strategies, track multiple concurrent projects, and ensure alignment between clients and engineering while documenting architectures and managing risks and escalations.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

The Senior SW QA Engineer will collaboratively ensure high quality in optical networking solutions through automated testing, Agile ceremonies, and continuous improvement of QA practices.

Support the Enterprise AI PMO by maintaining the AI initiatives portfolio, coordinating updates across teams, managing programme inbox and governance processes, preparing leadership reports and dashboards, surfacing risks and dependencies, and improving reporting and operational processes using AI-enabled tools to scale the programme consistently.

Lead prompt engineering initiatives and provide technical enablement to business users: develop prompt best practices, coach and train stakeholders, integrate AI tools into workflows, evaluate AI platforms, mentor junior staff, and document technical learnings and case studies.

Enable engineers to generate certified AR5 documentation using internal LLM tools. Act as human-in-the-loop to validate AI output for technical accuracy and aerospace/defence certification compliance. Build templates, authoring standards, version control and traceability, and collaborate with systems, avionics, RF, payload, and quality teams.

Owner of AR5 technical documentation: enable engineers to generate certified docs using internal LLM tools, validate AI-generated content for technical accuracy and certification compliance, build templates and docs-as-code practices, govern versioning and traceability, and collaborate with systems engineering, avionics, RF, payload and quality teams.

Provide English-language customer support for an AI advertising platform: assist users, solve technical issues, create positive experiences, join a multicultural team, and grow with training and renewable contracts.

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 the design, build, and deployment of AI-powered agents, workflows, and integrations for global marketing. Evaluate buy-vs-build, integrate platforms (HubSpot, Salesforce, Meta, Google, LinkedIn), optimize AI economics, and measure ROI while collaborating with marketing teams to automate and improve campaign execution and content localization.

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.

Evaluate Spanish AI-generated content for quality, factual accuracy, relevance, and consistency. Identify errors and guideline deviations, assess annotations and reviewer decisions, provide clear feedback, and participate in training and calibration while meeting project expectations.

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.

Design and implement enterprise intelligent automation using Dataiku and LLMs, architecting workflows, integrating APIs and cloud systems, industrializing pipelines with CI/CD and governance, building agentic AI solutions, and mentoring junior engineers.

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.