Top Artificial Intelligence Jobs in Lisbon

Leads end-to-end AI transformation deployments for mid-market clients across EMEA. Owns executive relationships, rollout cadence, adoption, measurable business outcomes, and client sign-off. Builds AI agents, automations, and data workflows hands-on against real client data, directing engineers when deeper platform work is needed. Manages multiple concurrent engagements, travels regularly onsite, and codifies deployment methods for repeatability.

Own end-to-end discovery and delivery for an AI assistant serving Brazilian e-commerce sellers. Conduct Jobs to Be Done research, launch agent-based services across commerce workflows, build prototypes with AI tools and UI components, analyze product performance, and improve retention and daily active usage. The role requires regular travel to Brazil for customer meetings and industry events and is based in or relocates to Lisbon.

Designs 3D models, prototypes, and visual assets for airport touchpoints, kiosks, and boarding systems. Uses SolidWorks, AutoCAD, Vizcom, and AI-assisted design tools to develop feasible product concepts. Collaborates with UX, product, engineering, and AI teams; presents concepts through visual storytelling; prototypes and validates designs; documents workflows; and participates in stakeholder workshops. The role requires occasional travel to airports or R&D sites and focuses on integrating AI into physical product and experience design.

Design and deliver cloud-based AI/ML systems with a focus on database architecture, scalable data pipelines, model training and production, generative and deep learning model customization, HPC algorithm R&D, data preprocessing, storage/retrieval design, and cross-functional infrastructure alignment including edge and HPC resources.

Operate and improve the reliability, availability, performance, and scalability of AI and Generative AI platforms across development through production environments. Responsibilities include monitoring, alerting, incident response, on-call support, SRE metrics, postmortems, automation, CI/CD, infrastructure as code, containerization, cloud modernization, troubleshooting, capacity management, and collaboration with development, cloud, infrastructure, security, network, and database teams.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Build and ship user-facing AI agent features for Android within Qonto’s regulated banking app. Own the Android experience end to end, designing clean, testable, performant Kotlin code and delivering features from scoping through release. Collaborate with Product, iOS, Backend, and Frontend teams to bring web capabilities to mobile, while using AI coding tools extensively and helping establish scalable foundations for other teams’ AI agents.

Build and operate production AI infrastructure powering intelligent agents at scale. Develop high-performance Rust services for model inference, orchestration, and execution; design scalable distributed systems; establish MLOps, observability, guardrails, and failure recovery; and optimize latency, throughput, reliability, and cost. Partner with agent systems and infrastructure teams to turn prototypes into hardened services serving millions of users.

Conduct research, market sizing, forecasting, competitive analysis, and thought leadership on enterprise AI services. Develop actionable insights for technology providers and senior executives through published research, presentations, conferences, and client engagements. Assess market trends, demand drivers, customer behavior, and growth opportunities while advising clients, supporting sales, reviewing content, mentoring analysts, and improving research processes.

Leads end-to-end architecture for enterprise data, analytics, and AI solutions in a Microsoft-first environment. Translates business requirements into secure, scalable designs and guides prototyping, implementation, architecture reviews, troubleshooting, governance, and production readiness. Requires deep hands-on experience with Azure data platforms, Power BI, enterprise security, Azure OpenAI, and retrieval-augmented generation patterns.

Design and deploy enterprise agentic AI systems, including MCP integrations, agent orchestration, secure tool execution, RBAC, identity propagation, policy enforcement, evaluation, and safety guardrails. Build production-ready platform adapters and integrations using Python, AWS, Docker, CI/CD, REST, SQL, and data modeling. Collaborate with developers, leadership, clients, and delivery teams while guiding adoption and maintaining engineering best practices.

Leads business analysis for complex fraud-risk technology implementations, gathering requirements, translating regulatory needs into system specifications, mapping data flows, and designing solutions with technical teams. Coordinates testing, configuration, training, adoption, and post-implementation improvements while advising senior financial-institution stakeholders. The role also facilitates workshops, manages alignment, mentors Business Analysts, and supports product and process enhancements.

Lead the strategy and roadmap for Wrike’s AI agent platform, including MCP, Skills, and GraphQL. Guide engineering teams, establish standards for secure and reliable agent interactions, enable internal teams to build tools, and manage MCP plugin distribution across AI marketplaces. Use customer research, adoption data, and ecosystem feedback to prioritize agent workflows and translate technical trade-offs into product decisions.

Manage influencer campaigns end to end across multiple brands and markets. Responsibilities include creator casting, briefing, negotiation, contracting, campaign timelines, budgets, approvals, client communications, performance tracking, reporting, and optimization. The role requires using CPV and campaign KPIs to manage commercial outcomes, ensuring content meets brand and compliance requirements, and operating as a power user of Kyra’s platform and AI tools.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Fine-tune and post-train open-weight LLMs using methods such as SFT, LoRA, QLoRA, DPO, and GRPO. Build training data and evaluations, diagnose model failures, improve personalization and multilingual behavior, accelerate reproducible training workflows, and deploy models to production. The role involves using real user behavior to guide iterations across a large-scale AI companionship platform, with collaboration across AI, product, web, DevOps, and leadership teams.

Lead technical delivery of an AI-assisted operations platform from discovery through implementation and evaluation. Establish technical baselines, validate integrations, design maintainable rules and AI workflows, guide engineers, review code, implement testing and observability, and ensure traceable human-approved decisions. The role requires hands-on architecture, API integration, evaluation, CI, production readiness, and clear communication with business and engineering stakeholders. Travel to an in-person working session in Milan is included.

Lead the architecture and implementation of AI-native product engineering systems, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step agentic features, and AI code-review optimization. Ship end-to-end product features, set technical direction, evaluate LLM reliability and trade-offs, and accelerate validated learning through close collaboration with engineering, product, and design teams.

Lead the architecture and implementation of AI-native product engineering systems, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step agentic features, and AI code-review optimization. Ship production software, establish technical direction, and partner with product and design teams to accelerate validated learning. The role requires strong backend and distributed-systems expertise, hands-on LLM product experience, and practical use of AI coding tools.

Own end-to-end product design for MEDvidi’s AI Track, creating human-AI interaction patterns for clinical tools. Gather requirements independently, conduct user analysis and validation, prototype directly in code using Claude Code or Cursor, collaborate with product and engineering, accept delivered designs, analyze outcomes, and contribute reusable Storybook design-system components, tokens, and documentation.

Join Welo Data’s Swahili contributor network for potential remote AI projects involving text, image, or audio annotation; search, advertising, or chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Applicants must be native or near-native Swahili speakers based in Kenya, Tanzania, or the Democratic Republic of Congo, with English proficiency, digital-tool comfort, and strong attention to detail. Projects vary in duration and compensation, with details provided before participation.

Deploys and integrates AI solutions using frontier LLMs and autonomous frameworks. Bridges AI research, software engineering, and product implementation; adapts models, designs scalable workflows, and ensures performance, reliability, and business alignment. The role requires collaboration with developers and team leads, with regular travel and extended stays across Samlino Group’s three markets.

Develop AI-driven products through hands-on software development and product engineering. Build minimum viable products, solve problems creatively, and help shape future AI applications. The role is available remotely or in Boston and Chicago, with contract options ranging from two weeks to three months or full-time, plus potential revenue sharing.

Lead the organisation’s AI strategy and data function, driving AI adoption, workflow redesign, self-service analytics, semantic data modelling, and trusted data access. The role manages Data Analysts, oversees the business-facing data warehouse layer, establishes analytics engineering standards, and delivers measurable insights and business value. It requires strong experience implementing AI solutions, cloud data platforms, SQL, dbt, financial services data, and stakeholder leadership in regulated environments.

Leads QA automation strategy across web, API, and backend services. Designs and maintains automated test frameworks, integrates testing into CI/CD pipelines, investigates defects, maintains test environments, and promotes quality practices. Mentors junior engineers and collaborates with product and engineering teams on acceptance criteria and release quality. Applies AI-assisted tools for test generation, debugging, defect analysis, and automation optimization.