Top Artificial Intelligence Jobs in Portugal

Lead engineering execution and technical direction while remaining approximately 75% hands-on. Responsibilities include developing LLM- and AI-powered products, coding, code review, debugging, platform performance, sprint planning, cross-functional coordination, technical mentorship, recruiting, and supporting engineers’ growth. The role partners closely with founders and product teams and offers a path toward Engineering Manager responsibilities.

Lead the architecture, full-stack development, delivery, scalability, and reliability of an invoicing and accounting platform. Own backend C#/.NET, frontend Vue.js, Azure infrastructure, testing, deployment, and AI integration. Resolve technical challenges, guide architectural decisions, support scalable AI workloads, and shape the company’s technology direction while communicating effectively with technical and non-technical stakeholders.

Own relationships with 40–50 enterprise EMEA clients, leading onboarding, product adoption, usage monitoring, renewals, and account expansion. Help build scalable customer success playbooks, manage NRR and GRR KPIs, and contribute to team OKRs. The role requires demonstrating value for technical SaaS products, navigating complex stakeholder environments, solving customer challenges, and driving commercial outcomes.

Design and improve mobile health coaching experiences focused on behavior change, AI-generated coaching, gamification, and user engagement. Collaborate cross-functionally to develop innovative UI/UX solutions, using user psychology, UX research, data analytics, and rapid experimentation to inform decisions. Generate and present original design hypotheses, validate solutions, and improve customer journeys, motivation, habits, and long-term loyalty.

Lead technical data science projects from scoping and proof of concept through production deployment. Develop robust machine learning models, collaborate with data engineering and MLOps teams, engage stakeholders, translate technical concepts, and mentor junior data scientists. The role requires Python and SQL expertise, advanced machine learning knowledge, cloud and big data platform experience, exploratory analysis, dashboarding, data engineering tools, and model deployment experience.

Leads global commercial customer data governance, Customer 360 strategy, segmentation, data quality, reporting, and insight initiatives. Owns customer definitions and hierarchies across SAP and Salesforce, establishes governance and KPI standards, and aligns reporting and analytics investments with business value. Partners cross-functionally to improve data quality, reporting adoption, forecast accuracy, and AI readiness while driving change management and value realization across the commercial organization.

Design, deploy, and optimize machine learning and AI solutions, including fine-tuning open-source and commercial LLMs, implementing RAG, and developing generative AI models. Conduct exploratory data analysis and feature engineering, apply traditional machine learning methods, validate solution quality, collaborate with engineers and stakeholders, and maintain technical documentation for reproducibility and knowledge sharing.

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.

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.

Leads the enterprise process excellence strategy, roadmap, governance, methodology, tooling, and capability development. Identifies and prioritizes opportunities to simplify, standardize, automate, and improve critical processes. Oversees performance and KPIs, leads selected improvement programs, evaluates automation technologies, coaches teams, and drives continuous improvement across a complex international insurance organization.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

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.

Create and evolve Noxus’s visual identity across brand, motion, video, product storytelling, and marketing collateral. Translate complex AI and enterprise technology into clear visual assets, including diagrams, reports, presentations, case studies, and demos. Build reusable design and motion systems, own the video pipeline, and collaborate with founders, marketing, sales, product, engineering, agencies, and external partners in an in-office startup environment.

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.

Create narrative-driven, long-form cinematic videos using AI-first workflows, from scripting and prompting through generation, editing, compositing, motion graphics, color correction, and final delivery. Maintain character and scene consistency across sequences, experiment with emerging AI video models, refine production workflows, and independently execute creative direction. The role includes stylized and explicit adult content and requires strong storytelling, technical judgment, rapid iteration, and collaboration.

Create narrative-driven, long-form cinematic videos using AI-first workflows, from scripting and prompting through generation, editing, compositing, motion graphics, color correction, and final delivery. Maintain consistent characters, styles, and scene continuity across sequences while experimenting with emerging AI video tools. Collaborate with creative leadership, work autonomously, refine production workflows, and produce stylized and explicit NSFW content.

Build production-grade agentic AI systems that analyze infrastructure discovery logs, execution results, errors, and failed discoveries. Develop AI agents for failure classification, root-cause analysis, troubleshooting, and corrective recommendations. Create telemetry data pipelines and REST API integrations, integrate generative AI, RAG, and vector databases, and implement testing and guardrails for production changes.

Build production AI agents that analyze infrastructure discovery logs, correlate failures, troubleshoot issues, identify root causes, and recommend corrective actions. Develop data-processing pipelines and REST API integrations for telemetry, integrate generative AI APIs using RAG and vector databases, and implement testing, guardrails, CI/CD, security scanning, and deployment automation.

Build production-grade agentic AI systems that analyze infrastructure discovery logs, execution results, errors, and failed discoveries. Develop AI agents for failure classification, root-cause analysis, troubleshooting, corrective-action recommendations, and guarded probe or pattern updates. Create data-processing pipelines and REST API integrations to feed monitoring telemetry into AI systems. The role requires advanced Python, GenAI API integration, RAG, vector databases, CI/CD, testing, and software delivery expertise.

Build production-grade applications, APIs, data pipelines, and AI-powered solutions from prototype through deployment. Develop backend services with Python and SQL, integrate enterprise systems and AI platforms, and contribute to LLM, RAG, agentic, and automation workflows. Collaborate with commercial and sales teams to translate business needs into scalable technical solutions. Preferred work includes Power BI modeling, data engineering across cloud platforms, CI/CD, infrastructure as code, and MLOps.

Build Solidgate’s new ML/AI function, developing production machine learning models, recommendation systems, and real-time transaction-routing decision systems. Own experimentation from ideation through A/B testing and deployment, engineer features from high-cardinality transactional data, integrate emerging AI technologies, and collaborate with Product, Engineering, and Data Platform teams on training and analytics pipelines.

Own the vision and strategic roadmap for GoCardless payment-success products powered by AI and machine learning. Translate customer insights into data-driven solutions, partner with data platform teams to build foundational infrastructure, and enable sales and support teams to communicate product value. The role requires shaping technical direction, improving payment performance, solving complex customer challenges, and communicating strategy clearly to executives, stakeholders, and customers.

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.