Top Artificial Intelligence Jobs in Lisbon

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

As a Senior AI Engineer, you will design and maintain AI capabilities, develop infrastructure, create evaluation frameworks, and collaborate across teams to integrate AI into products.

The Chief Data & AI Officer will lead the establishment of a company's data and AI infrastructure, ensuring reliable information capture, decision-making processes, AI system integration, and compliance with governance standards.

Serve as the bridge between clients and technical teams to deploy and optimize Feedzai's fraud detection platform. Gather requirements, translate compliance needs into specifications, define data mappings, design and configure solutions, lead UAT, deliver training, and drive continuous improvement to enhance alert efficiency and investigative workflows.

Lead design and deployment of AI agents and workflow automations across Supply & Operations, coach and lead Operational Excellence (Lean/Six Sigma) projects, build OE capabilities, embed continuous improvement and AI-driven workflows into enterprise systems, and lead global OE programs and strategies in External Supply.

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.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Own end-to-end feature delivery in an AI-first engineering team: direct and validate AI across the build loop, set quality standards for AI-generated code, develop prompts and agent workflows, mentor junior engineers, and collaborate with PM, design, DevOps and QA to ship production software.

Lead and grow a Lisbon-based engineering team building an AI-first product development lifecycle platform. Own roadmap, delivery, metrics, backlog prioritization, and adoption of AI tools and managed agents. Translate stakeholder needs, measure outcomes, and be accountable for engineering productivity and product impact.

The AI Lead Product Manager will define and own the product strategy for AI-powered health data, lead product teams, engage with stakeholders, and ensure compliance with regulations.

The AI Programme Manager will oversee AI initiatives across health systems, coordinating delivery, managing stakeholders, and ensuring alignment with strategic goals, while mitigating risks and driving timely execution.

Design and implement generative AI and ML solutions, build multimodal agents and automated workflows, develop MLOps tooling and cloud deployment pipelines (Azure/AWS/GCP), use Python/Databricks/SQL/PySpark/Docker/LangGraph, document work and present results to stakeholders.

Design, build, and deploy production LLM-driven systems for requirements and hardware engineering. Develop agentic flows, benchmark and evaluate models, build evaluation pipelines and tests, optimize cost and latency, and collaborate with cross-functional teams to ship and continuously improve AI features.

Lead a new AI Ops team (start with two engineers) to build, ship, and scale automations and agentic tooling across internal operations; prioritize requests, maintain AI tooling health, integrate AI into products, train teams, and demonstrate measurable value.

We are seeking an experienced AI Engineer with a focus on Generative AI to develop scalable solutions. Responsibilities include AI development, programming in Python, LLM integration, and API integration.

Design, deliver, and optimize global digital and blended learning programs using LMS and AI tools. Manage end-to-end program delivery, collaborate cross-functionally, analyze learning data for impact, and improve learning operations and scalability of the FlixUni platform.

Lead development and measurement of Fever's AI search presence by designing tests and analytics to track LLM visibility, collaborating with Product/Engineering/Data to optimize schemas and APIs for AI ingestion, and executing PR/community strategies to shape LLM perception and generative citations.

Serve as the AI technical expert for Zendesk customers: design, configure, integrate, and optimize Zendesk AI solutions. Lead end-to-end project delivery, drive adoption and change management, use analytics and predictive models to surface churn/expansion signals, train stakeholders, and collaborate cross-functionally to remove blockers and deliver measurable business outcomes.

Lead end-to-end delivery and adoption of Zendesk AI solutions as a technical consultant. Provide configuration guidance, integrate APIs, run projects, enable change management and training, use analytics to predict churn/expansion, and liaise with Customer Success and Sales to drive measurable business outcomes and high customer satisfaction.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

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.

As an AI/ML Engineer, you will develop and optimize machine learning models for mobile applications, collaborate with teams, and analyze data to enhance user experience.

As an AI Product Manager, you will lead the development of AI-powered features, collaborating with cross-functional teams to align products with user needs and business goals, while overseeing the product lifecycle.

Lead deployment and integration of AI solutions, bridge research and engineering, design scalable AI workflows, integrate and validate model performance and reliability, and collaborate across teams while travelling to support implementation across markets.