Top Artificial Intelligence Jobs in Lisbon

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Design, build, and run production agentic workflows and reusable components for delivery processes. Implement RAG-backed knowledge bases, monitoring, evals, observability, and human-in-the-loop feedback to improve agents and integrate them with delivery teams and business tools.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Work with the Platform Owner to translate go-to-market needs into AI-augmented internal tools: gather requirements, write functional and technical specs, define acceptance criteria, coordinate AI-assisted implementation, run automated quality checks, enforce architecture and design standards, and keep system documentation current while collaborating across US and EMEA stakeholders.

Evaluate Spanish AI-generated content for quality, factual accuracy, relevance, and consistency. Identify errors and guideline deviations, assess annotations and reviewer decisions, provide clear feedback, and participate in training and calibration while meeting project expectations.

Design and implement AWS-based data platform components, build multi-agent automated ETL/ELT pipelines, enable LLM-augmented capabilities (RAG, semantic search), collaborate with engineers, analysts and data scientists, ensure data quality, security, and production readiness.

Build and operate the internal AI platform used by 600+ developers: secure AI coding environments, AI-assisted CI and agentic AWS workflows. Own products end-to-end, improve developer experience, ensure reliability/observability/cost-awareness, participate in on-call, and capture reusable patterns and templates for broad adoption.

Build and ship end-to-end AI features: design prompts, tools, memory, and agent workflows; convert model outputs into reliable behaviors; debug model, infra, and UX issues; optimize latency, cost, and reliability; develop evaluation frameworks and collaborate with product and engineering to deliver production ML systems.

Build end-to-end AI-native product features across frontend, backend, and model integrations. Design and deploy agent workflows, integrate LLMs, memory, and external tools, improve real-time streaming, latency, reliability, observability, and fallback mechanisms, and iterate on failures to deliver dependable, multi-step task completion in production.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Lead short-term AI adoption program: build internal AI champions, create usage policies and training, support teams implementing AI tools, contribute to internal AI tool builds, and ensure responsible, secure deployment across distributed teams.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Produce high-quality AI-generated visuals for performance marketing across paid channels and landing pages. Create photorealistic human and lifestyle imagery, iterate based on performance data, integrate AI outputs into design pipelines, and collaborate with marketers, copywriters, and video teams to deliver conversion-focused creative variants.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

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.

As an AI Product Manager at n8n, you'll develop AI-driven product experiences, coordinate with various teams, and innovate through user-centric design, focusing on AI trust and building.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Create short- and long-form AI-generated educational and entertainment content for Instagram, TikTok, and YouTube. Rapidly test content hypotheses, analyze performance, research trends and competitors, and iterate on hooks, storytelling, and editing. Collaborate with the product team and continually experiment with AI image/video generation and editing tools.

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.