Top Artificial Intelligence Jobs in Lisbon

Design, build, evaluate, and deploy production-grade LLM and classical ML solutions. Create retrieval and agentic workflows, integrate AI into client apps, implement evaluation frameworks, optimize prompts and pipelines, and collaborate with product teams through discovery to production support.

Lead design and ownership of shared libraries, Spring Boot starters, and agentic frameworks. Set standards for agent orchestration, evaluations, and guardrails. Mentor teams on Python-based agents (LangChain/LangGraph), drive adoption, prototype research directions, and improve developer experience across Kotlin backend and client libraries.

Provide EU-focused counsel on AI and privacy regulation (EU AI Act, GDPR, DSA); draft policies, DPA and contractual terms; translate legal requirements into technical and operational controls; advise product, engineering, and go-to-market teams; liaise with regulators and manage compliance programs and risk assessments.

Design and deliver cloud-native AI and LLM-based solutions from discovery through production. Lead presales, architecture, RAG and agentic implementations, deploy and operate ML/LLM systems, write production-grade Python, build ETL/ELT pipelines, implement MLOps/LLMOps/AgentOps, mentor engineers, and manage client technical relationships.

As an AI Engineer, you'll design and build AI systems for production applications, focusing on client solutions and maintaining system performance through evaluations and observability.

This role involves managing Microsoft Commercial Marketplace integrations, evaluating opportunities, conducting feasibility assessments, and leading R&D for novel API integrations and enhancements.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

Design, build, and maintain scalable backend systems and APIs for specialized AI assistants (helpdesk, HR, back office). Ensure secure data handling, integrate LLM-driven features, collaborate with front-end teams, and participate in agile processes, code reviews, and continuous architecture improvements.

Define and lead implementation of scalable, secure AI/GenAI architectures (LLMs, RAG, vector DBs, agents). Set technical standards, build PoCs, integrate models with enterprise systems, ensure governance, and mentor engineering teams while supporting presales and client engagements.

Develop and maintain a multi-cloud GenAI platform: design, implement, test, deploy scalable APIs and backend services, build monitoring and KPIs, automate workflows, ensure security/compliance, and collaborate with international teams to continuously improve platform capabilities.

Design and build AI-driven conversational flows and integrations using LLMs, prompt engineering, REST APIs, and modern backend practices. Collaborate with multidisciplinary teams to influence product direction, write and refactor scalable code, and implement solutions in cloud environments (Azure/AWS).

Lead design and build Generative AI applications using LLMs and Azure AI services. Implement, deploy (Azure ML, CI/CD), integrate semantic search and embeddings, handle data engineering, and present solutions to stakeholders.

The AI Creative Executor will lead AI-enabled marketing campaigns, blending storytelling with technology, managing end-to-end production and collaborating with the marketing team to ensure alignment with brand standards.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Provide research and advisory to global CFOs and Controllers on transforming controllership processes (AP, AR, financial close) using AI, automation and finance technologies; produce published research, vendor evaluations, and client-facing presentations; collaborate with analysts and deliver strategic, actionable recommendations to improve financial reporting and automation outcomes.

Lead product development in the industrial sector focusing on IoT, edge computing, and AI applications. Oversee product lifecycle, conduct market research, and collaborate with cross-functional teams.

Lead engineering enablement across eight companies to cascade CTO AI vision into team discipline. Detect and package successful practices, run cross-company diffusion, operate cadence of validation and demos, measure adoption and outcomes, triage deeper needs to PoCs/workshops, and capture reusable artifacts and metrics.

As a Staff/Senior AI Product Engineer, you will build AI-powered product features, enhance the tech stack, and design AI workflows while collaborating with product teams. Your focus will be on agent-native architecture and improving the codebase for AI agents.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Serve as a strategic partner to AI Agents product leadership to align customer insights, product priorities, and cross-functional initiatives. Drive operational excellence by optimizing existing product development programs, improving processes, enabling data-informed decisions, and producing executive-ready recommendations. Coordinate across Product, GTM, Data, and Customer Experience to improve discovery, planning, launches, and customer engagement while measuring impact of improvements.

Design and build production-grade multi-agent AI systems: orchestration, routing, memory, tool execution, multimodal workflows, and integrations with model providers and platform APIs. Implement testing, observability, and reliability practices; analyze and extend APIs and codebases to improve agent behavior, performance, and robustness in gaming and transaction-heavy domains.

Manage curriculum development for data science and engineering, collaborate with instructors, create content, and drive improvements based on learner feedback.

As a Curriculum Manager, you will manage content development for the data science and engineering curriculum, collaborating with instructors and teams to enhance learning experiences.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Own and improve Primer's GTM intelligence: maintain dashboards and semantic models, train BI/LLM models (Omni/Claude), surface pipeline/account signals, ensure correct Salesforce modeling, build revenue-driving models and GTM AI workflows, partner with data engineering and RevOps, drive adoption, and promote self-serve data literacy.