Top Artificial Intelligence Jobs in Lisbon

Own product areas for AI stockbroking and related financial services. Define user problems, product direction, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, ship features, monitor performance, and iterate based on data, feedback, research, and production issues. The role requires strong ownership, prioritization, stakeholder management, product judgment, and effective execution with limited guidance.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Own AI Investing product areas across financial services, defining user problems, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex products, launch features, monitor performance, and iterate using data, feedback, research, and production insights. The role requires strong product judgment, execution, prioritization, communication, stakeholder management, and ownership in a fast-moving remote environment.

Own product areas within an AI neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, roadmaps, metrics, scope, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; launch features; monitor performance; and drive continuous improvements using research, feedback, data, and production insights.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and related services. Define user problems, product direction, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; launch features; monitor performance; and continuously improve products using data, feedback, research, and production insights.

Own product areas for BJAK’s AI Stockbroking and financial services products. Define user problems, roadmaps, success metrics, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; and use data, feedback, experiments, and production insights to improve products after launch.

Own product areas within BJAK’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define product direction, user problems, roadmaps, metrics, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams. Use research, feedback, experiments, and performance data to improve products and make trade-offs among speed, usability, business impact, compliance, and operational complexity.

Own AI Investing product areas across financial services, defining user problems, product scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, launch features quickly, monitor performance, and iterate using data, research, feedback, and production issues. Balance speed, user experience, business impact, compliance, and operational complexity while driving execution with high ownership.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and related financial services. Define user problems, product strategy, scope, metrics, roadmaps, and launch plans while collaborating with engineering, design, data, compliance, operations, and business teams. Simplify complex financial products, launch features, monitor performance, use research and feedback, and drive continuous iteration and prioritization.

Provide technical support for ClickHouse and Langfuse customers across EMEA and global regions through cases, chat, Slack, community channels, and calls. Troubleshoot database, cloud infrastructure, AI application, tracing, and evaluation issues within SLA and on-call rotations. Develop documentation and educational content, assist with technical proofs of concept, mentor colleagues, escalate bugs, collaborate with Engineering and Product teams, and support customer implementations directly in codebases.

Own the architecture and production delivery of a real-time voice AI copilot. Lead streaming speech-to-text, LLM extraction, Chrome extension integration, AWS infrastructure, latency optimization, evaluation, observability, cost control, concurrency, data isolation, monitoring, and rollback. Lead the AI engineering pod, manage client stakeholders and scope, present evidence-based results, deliver production epics, and transfer knowledge to client and internal teams.

Lead end-to-end development of an AI mahjong companion, including the gameplay decision model, LLM explanation layer, valid-action integration, win detection, training pipeline, evaluation harness, and low-latency serving. Own architecture, AWS deployment, Langfuse observability, and the two-second response budget while leading a small AI pod and communicating technical decisions to the client’s CTO and engineering team.

Lead end-to-end product design for complex enterprise products, rapidly prototyping near-functional AI-assisted workflows, collaborating with engineers, presenting to stakeholders, and delivering accessible, compliant designs in a hybrid Lisbon setup.

Join as the first Lisbon-based full stack generalist to build and scale an AI-powered patient recovery platform. Work across AI agent orchestration, prompt engineering, telephony/messaging integrations, dashboards, campaign management, and clinic system integrations to expand the product across markets.

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.

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.

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.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

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.

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.

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.

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.

Lead architecture and delivery of a production-grade generative AI media platform. Build reproducible, versioned pipelines (node-based or code-first), train LoRAs, integrate control techniques, add automated QC against product references, enable one-click tools for non-technical teams, and own the technical roadmap with design partners.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.