Top Artificial Intelligence Jobs in Lisbon

Architect and build an AI-native automation and orchestration toolchain (fusing traditional tools with foundation models), enable and mentor early adopter teams, standardize practices across departments, drive infrastructure-as-code and CI/CD adoption, contribute to the IT/Sec operating system, and champion cross-departmental adoption of automation and AI platforms.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

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.

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.

The Principal AI/ML Scientist & Engineer will lead a team, focusing on AI/ML strategy and developing data products for business outcomes.

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.

Design, prototype, and scale AI-assisted workflows to optimize content production and growth funnel operations. Build and deploy no-code automations, integrate generative AI across CRM and creative pipelines, coordinate cross-functional teams, audit and simplify legacy processes, manage vendors and tooling, and establish operational metrics to track pipeline velocity.

Responsible for end-to-end B2B demand generation: design and run multi-channel paid acquisition, optimize funnels and landing pages, run ABM and field marketing, partner with sales, and use AI/LLM tools to automate and scale campaign execution and lead quality.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.

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.

Build, deploy, monitor, and optimise production-grade agentic and LLM systems to detect and prevent risk and fraud. Design agent orchestration, RAG pipelines, and LLM serving while writing production-quality Python, querying large structured datasets with SQL, and implementing ML evaluation, observability, and graceful degradation.

Lead Cohere's policy engagement on compute infrastructure, export controls, sovereign AI, and national AI competitiveness. Monitor and analyze relevant developments, develop policy positions, engage governments and cross-functional teams, represent Cohere in public discussions, and produce policy analysis and thought leadership to inform strategic business opportunities and public-private partnerships.

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.

Join COLIBRIX.ONE as a Manual AI-Assisted QA Engineer focusing on fintech solutions, validating payment and wallet flows through various testing methods while leveraging AI tools for optimization and efficiency.

The role involves designing and operating AI solutions, such as LLM-enabled workflows and integrated systems, ensuring production standards.