Top Artificial Intelligence Jobs in Lisbon

Design polished, user-centric mobile and web interfaces for BJAK's AI and financial products. Create wireframes, flows, prototypes and high-fidelity UI, maintain the design system, collaborate with PMs and engineers, iterate using feedback and analytics, and ensure accessibility and responsiveness across devices.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

Design and build scalable, accessible frontend applications for AI-driven Contact Center and UC products. Implement responsive interfaces with Angular/React, integrate with backend AI services (REST/GraphQL), optimize performance, participate in architecture and code reviews, and collaborate with cross-functional teams in an Agile environment.

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.

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.

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.

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.

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.

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.

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.

Design and scale GTM systems and AI-powered workflows across sales, marketing, and product. Build agents, integrate Salesforce/HubSpot, turn product and user data into growth signals, create lead-scoring/routing/onboarding flows, identify bottlenecks, run experiments with founders to drive measurable revenue impact.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

Build and productionize AI-native systems for a fintech neobank: AI agents, LLM apps, RAG/retrieval, tool-calling, automations and AI-assisted experiences for onboarding, KYC, support, risk, CRM and operations while addressing accuracy, latency, cost, security and compliance.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

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.