Top Artificial Intelligence Jobs in Lisbon

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

Lead strategic product roadmaps, conduct market intelligence, manage pricing strategy, apply data analysis, and mentor teams while collaborating with cross-functional partners.

Design, build, and ship customer-facing LLM features and agentic workflows end-to-end. Implement RAG systems (chunking, embeddings, retrieval, re-ranking), safe production deployment, evaluation pipelines, drift detection, and observability. Advise on retrieval architecture, drive Context Development Lifecycle practices, and embed as a hands-on individual contributor to raise squad-wide AI quality and adoption.

Author and iterate curriculum for experienced engineers transitioning to system architecture: validate syllabus, define learning objectives, produce AI-assisted lessons, design hands-on projects, write rubrics and reviewer guides, and collaborate with LX designers to refine content. Part-time contract (15–20 hrs/week) over ~4–6 months.

Lead live sessions, mentor senior engineers transitioning to architecture roles, review and assess projects, run architecture review boards, provide technical escalation and detailed feedback, record instructional content, and run mock interviews and office hours to ensure students meet program standards.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

Build core AI platform capabilities (agent runtimes, model routing, tool registries) and production-grade services and tooling using Node.js, TypeScript, NestJS and React. Develop reusable AI infrastructure, work with LLMs and LangChain/LangGraph, and own features end-to-end to enable secure, scalable AI across the company.

Build and own AI/LLM-assisted product features in Ruby on Rails: integrate LLM APIs, implement prompts and retrieval patterns, ensure reliability, security, and performance, write automated tests, debug production issues, and improve system behavior over time.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Lead and scale demand generation by building signal-driven, automated GTM systems using AI and no-code tools. Own MQL-to-pipeline metrics, optimize CAC, run performance campaigns, integrate CRM and enrichment workflows, collaborate with sales/RevOps to improve conversions and pipeline ROI.

Remote SDR responsible for outbound prospecting, qualifying leads, booking demos, maintaining CRM records, collaborating with marketing and sales, and meeting monthly KPIs in the AI SaaS space.

Lead development of scalable AI-powered GTM systems, migrating from no-code to Claude Code agent architectures. Build integrations, data pipelines, and reusable agent frameworks; ensure reliability, observability, and cost optimization. Hire, mentor, and manage a distributed engineering team while evaluating emerging AI tools and driving platform innovation.

Lead design, build, and scale client-facing and internal automation systems. Create advanced workflows (GoHighLevel, n8n/Make/Zapier), integrate platforms via APIs/webhooks, document SOPs, advise clients, maintain CRMs and troubleshoot live systems for reliability and scalability.

Design, develop, and deploy AI/ML models and LLM-powered workflows (chatbots, copilots, automation). Integrate models with APIs and SaaS platforms, fine-tune prompts, work with structured/unstructured data, monitor performance, and collaborate with product and engineering teams to deliver scalable AI solutions.

Build end-to-end AI-first features for a benefits platform, working across frontend (React/TypeScript) and backend (Python/FastAPI/SQLAlchemy). Ship production code, integrate ML components (LLMs, recommenders, RL), maintain CI/CD and cloud deployments (GCP preferred), collaborate with product/design, write tests and documentation, and participate in peer review and iterative development.

As an AI Engineer, you'll design AI tools for authors, enhance internal workflows, and build tools for AI development, collaborating with teams to identify AI opportunities.

As a Curriculum Manager, you will manage the content development lifecycle, collaborate with instructors, and improve data science curriculum, ensuring technical accuracy and pedagogical effectiveness.

Own and scale AI enablement: build core orchestration, LLMOps/MLOps platforms and governance, integrate generative AI into product, optimize cost/latency, run PoC-to-production delivery, upskill teams, standardize tooling and vendor strategy, and track adoption/ROI metrics.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

The Principal AI Engineer will build and improve the core AI tutoring system, design evaluation methods, and collaborate on pedagogical goals, driving innovation in learner experiences.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.