Top Artificial Intelligence Jobs in Portugal

Own product marketing for Cyara’s Agentic AI assurance products, including positioning, messaging, go-to-market launches, competitive intelligence, sales enablement, technical content, and cross-functional collaboration. The role targets enterprise buyers through sales-led motions and requires measurable pipeline impact, strong writing and presentation skills, and comfort working with technical teams. Experience in AI, contact centers, QA, testing, observability, or monitoring is advantageous.

Produces daily 60–90 second AI-generated micro-drama episodes for TikTok, Instagram Reels, and YouTube Shorts. Responsibilities include developing striking visuals, consistent characters, natural voices, audio, purposeful editing, and story-driven visual styles. The role requires prompt engineering, strong editing fundamentals, independent execution, fast delivery, and familiarity with vertical video. Experience in traditional video production, dramaturgy, end-to-end AI video creation, and short-form drama is advantageous.

Design, build, and operate production AI systems including LLM agents, retrieval pipelines, event intelligence, and real-time inference services. Own architecture, orchestration, evaluation, guardrails, observability, reliability, and cost optimization at scale. Partner with platform, product, and research teams, and provide technical mentorship while developing resilient distributed systems for high-volume event streams.

Build and operate Kraken’s internal AI platform, including governed integrations, authentication, internal tooling, evaluation systems, spend controls, identity and access controls, provisioning, vendor configuration, and policy enforcement. Provide technical enablement through documentation, deep dives, and office hours while supporting teams and administering the enterprise AI estate.

Own end-to-end infrastructure delivery for strategic physical AI customers, from discovery and architecture through production rollout. Build cloud compute orchestration, platform services, onboarding environments, observability, security, and cost controls for simulation, training, inference, and batch workloads. Debug across infrastructure layers, productize recurring customer needs, and shape the physical AI platform roadmap. Partner with customers, Field CTO, Product, and Engineering while using AI coding tools to accelerate production engineering.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Lead global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

Build and operate production AI agents for fund-operations workflows, including architecture, orchestration, evaluation, observability, deployment, authorization, and human-in-the-loop controls. Develop shared AI platform capabilities, regression testing, provider fallback, regional failover, and document extraction. Improve agent accuracy, latency, and cost while establishing reusable engineering patterns and mentoring other teams.

Build Python-based backend services, APIs, and integrations that embed AI capabilities into a SaaS platform. Develop scalable customer-facing features, connect AI outputs to workflows and dashboards, collaborate with product, design, and platform teams, and implement observability, monitoring, and error handling. Contribute to the AI integration stack, including SDKs, MCP servers, and LLM orchestration.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

Analyze production Voice AI agent behavior using transcripts, logs, outputs, and operational data. Investigate hallucinations, failures, anomalies, and quality degradation; develop AI evaluation frameworks, monitoring systems, and dashboards; measure business and patient experience impact; and translate findings into product improvements with Product, Engineering, QA, and Operations teams.

Owns commercial success, revenue, and strategic relationships for embedded HR technology partners. Responsibilities include managing a $10M+ annual bookings target, developing partner sales motions, enabling partner sellers, driving pipeline and ARR growth, resolving commercial issues, leading executive business reviews, expanding partnerships, coordinating cross-functional teams, and managing partner account managers and embedded sales leads.

Designs and evolves AI-agentic product development frameworks, orchestrates AI agents and multi-agent systems, and develops secure, tested software using prompt engineering, RAG, and tool calling. Builds MCP servers and tooling, integrates APIs, implements continuous delivery pipelines, troubleshoots systemic issues, documents architectures, collaborates with stakeholders, and mentors global engineering teams.

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 the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.

Design, build, and operate production-ready AI systems and agentic applications using LLMs, retrieval pipelines, orchestration, and tool integration. Evaluate models and system quality, implement responsible AI controls, and ensure reliability through monitoring, testing, observability, and failure analysis. Collaborate with product teams and domain experts to deliver scalable AI solutions, define requirements, communicate limitations, and promote AI engineering standards across teams.

Develop and implement artificial intelligence solutions using machine learning, generative AI, and large language models. Translate business needs into scalable AI applications, work with data processing and APIs, evaluate and improve models, and operate within modern cloud environments and Agile teams. Strong English communication and continuous learning are expected.

Lead product ownership and project management for innovative banking tools and company culture initiatives. Define product vision, roadmaps, budgets, and requirements; coordinate business, engineering, design, and data science teams; oversee project plans, risks, resources, timelines, and quality; monitor product performance and user feedback; communicate progress to senior management; and apply knowledge of AI technologies to product development.

Develop scalable backend applications and REST APIs powering secure AI assistants for Helpdesk, HR, and Back Office operations. Integrate generative AI capabilities, collaborate with frontend teams, evolve backend architecture, and support performance and reliability improvements. Participate in Agile ceremonies, code reviews, sprint planning, and continuous improvement. The role requires Python and FastAPI experience, cloud and containerization knowledge, and familiarity with LLM integration frameworks.

Build and deploy reliable AI-powered internal tools, automations, workflows, and integrations across Sales, Finance, Customer Success, HR, and Product. Translate business needs into production-ready solutions, connect data across systems, evaluate and monitor LLM applications, and improve adoption and scalability. Collaborate with business teams to prioritize use cases, establish responsible AI practices, and continuously experiment with emerging models and tools.

Deliver AI fluency training, coach engineering teams on AI-assisted development, pair with developers, maintain internal AI agents and workflows, create enablement content, assess adoption maturity, and track productivity improvements. Collaborate with Architecture and Platform teams to support AI-first engineering practices and broader developer experience initiatives across the software delivery lifecycle.

Own the AI cost visibility product end-to-end, building Go backend services, APIs, and TypeScript/React interfaces. Translate cloud AI spend and usage data into clear FinOps experiences, while driving architecture, reliability, monitoring, and iteration. Collaborate closely with customers and product stakeholders, use AI throughout the engineering workflow, mentor peers, and shape product direction in an ambiguous market.

Own the AI cost visibility product end to end, developing Go backend services, APIs, data processing, and TypeScript/React interfaces. Translate AI spending data into clear FinOps experiences, work closely with customers and product needs, and use AI throughout the engineering workflow. Lead feature design, implementation, release, monitoring, and iteration while setting engineering standards, mentoring peers, and building scalable cloud-based SaaS capabilities.

Own AI cost visibility features end-to-end, from customer problem discovery and system design through implementation, release, monitoring, and iteration. Build scalable Go backend services, APIs, and TypeScript/React interfaces that process and present cloud AI spend data. Apply AI throughout the engineering workflow, contribute to product direction, understand FinOps and AI economics, mentor peers, and uphold reliable engineering practices.

Build and own production-scale LLM pipelines and agent systems for application security. Design RAG, retrieval, context, and memory strategies; evaluate models and agents; assess emerging AI tools and frameworks; and make build-versus-buy recommendations. Ensure reliability through guardrails, validation, fallbacks, and observability. Contribute to architecture discussions and code reviews while developing cloud-native AI features in Go and/or Python.