Top Artificial Intelligence Jobs in Lisbon

Build and maintain enterprise web applications using .NET and Angular, participate in full SDLC, integrate AI services and APIs, leverage AI-powered development tools and LLM/RAG solutions, contribute to architecture and technical decisions, collaborate with clients and delivery teams, and ensure compliance with enterprise/regulatory standards.

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.

Lead the Portugal COE innovation roadmap: run workshops, mentor teams, prototype and build FOAK automations using Google Apps Script and LLM-assisted coding, and operationalize experiments into deployed, documented, billable solutions.

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.

The Product Manager for AI Productivity will identify, prioritize, and implement AI-driven initiatives, focusing on improving operational efficiency at Salsify.

Lead end-to-end AI agent product development for Sales and Marketing: define roadmap, own backlog, drive discovery-to-adoption, lead engineers, partner with UX, and ensure secure, scalable production deployments.

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.

Build and own end-to-end features for Chainguard's AI-enabled CI/CD and supply-chain security products. Design, implement, and iterate on integrations like hardened GitHub Actions, container tooling, and agentic AI features. Drive architectural decisions, collaborate with Product and front-end teams, and ship secure, production-ready developer tools.

Lead design and implementation of CI/CD and AI-related product features, owning end-to-end delivery. Build and secure CI/CD systems, work with containers, registries, and Kubernetes, extend agentic AI into customer experiences, guide architecture, and collaborate closely with Product and front-end teams.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Build, deploy, and maintain AI-powered marketing workflows and HubSpot automations to replace manual tasks, improve campaign execution, enable sales, ensure data quality, and deliver measurable performance improvements.

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.

Lead the AI function, define roadmap, and build production generative AI systems (voice, chat, document). Split time between hands-on engineering (~40%) and leading the AI team, establishing RAG/agentic workflows, evaluation, observability, and best practices. Collaborate with product and engineering to deliver reliable, user-focused AI features.

Lead end-to-end technical projects for a Voice AI CX platform: scope, plan, manage releases, align product to customer requirements, track metrics, ensure quality, and communicate status to clients and engineering.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

The role involves architecting AI solutions for compliance, ensuring regulatory adherence, automating processes, and enhancing team capabilities through technology and training.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

Lead product strategy and execution for AI-enabled insurance products. Define 6–12 month direction, own P&L-impacting programmes, embed experimentation and AI playbooks, mentor PMs, and partner across engineering, data science, design, legal, and leadership to deliver measurable commercial outcomes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Lead product direction and roadmaps for major AI Neobank app areas. Own prioritization, execution, launch quality, and outcomes across multiple squads. Guide PMs, designers, and engineers; translate regulatory and business constraints into product decisions; drive post-launch learning and continuous improvement.