Top Artificial Intelligence Jobs in Lisbon

Own the vision, roadmap, business case, pricing, P&L, and go-to-market strategy for new software-defined rail products. Identify market opportunities, translate customer needs into requirements, and guide solutions from concept to first paying customers. Coordinate engineering, safety, certification, commercial, and business development teams while balancing certification milestones and customer commitments in a regulated, safety-critical environment.

Own the Vault product roadmap and lifecycle for a sports media asset platform. Lead discovery with customers, define solutions for content ingest, storage, integrations, AI enrichment, metadata, search, rights, and asset monetization. Partner closely with engineering, sales, and customer success, attend customer events, and prioritize product bets that help sports organizations maximize the value of extensive media archives.

Own enterprise customers’ AI adoption roadmaps from deployment through expansion. Partner with executives to define AI strategies, coordinate Product, Services, Sales, Success, and partners, and guide solution design and technical escalations. Track adoption, automated resolution, retention, and expansion metrics to drive measurable outcomes. Advise on responsible AI, data governance, compliance, and sustainable implementation while leading success planning, stakeholder alignment, and renewal-focused engagements.

Leads research and thought leadership on AI-driven HR strategy, transformation, governance, workflow redesign, regulatory environments, and value realization. Produces actionable insights across written, visual, video, and presentation formats; advises CHROs and senior HR leaders; analyzes market trends, vendors, technologies, and client challenges; supports sales and conferences; mentors analysts; and improves research processes.

Own daily operations and quality assurance for an internal AI-powered growth platform. Test releases, rule changes, edge cases, and regressions; document and verify bug fixes; monitor platform and agent activity; manage approval queues; evaluate agent output; escalate risks; and maintain test plans, QA checklists, runbooks, and release notes.

Optimize LLM inference on modern GPUs by analyzing GPU architecture, memory access, kernels, and performance bottlenecks. Develop, debug, reverse-engineer, and benchmark CUDA kernels and low-level operations for improved latency, throughput, and utilization. Work with inference frameworks including vLLM, SGLang, TensorRT-LLM, Triton, and FlashAttention. Investigate advanced topics such as distributed inference, quantization, KV cache optimization, and custom GPU kernels.

Build and deploy production machine learning systems for classification, retrieval, ranking, personalization, and evaluation. Develop efficient models and pipelines, improve search relevance and personalization, monitor production performance, debug issues, and ship measurable quality improvements while balancing accuracy, latency, cost, privacy, and reliability.

Build and deploy production machine learning systems for AI-native applications. Responsibilities include classification, retrieval, ranking, personalization, model evaluation, embedding-based search, fine-tuning, and training, inference, and deployment pipelines. The role emphasizes improving accuracy, relevance, latency, cost, and reliability; monitoring production performance; debugging issues; and using real-world feedback to drive measurable model improvements.

Lead end-to-end AI initiatives across Commercial Intelligence, from prioritization and proof-of-concept through production and adoption. Drive AI transformation, identify and scale use cases, evaluate tools, align technical and non-technical stakeholders, and communicate AI concepts to leadership. Build an AI-first culture through workshops and communities of practice while ensuring rigorous attribution and measurement. Partner with data science and engineering teams to deliver measurable business outcomes.

Leads end-to-end AI initiatives across Commercial Intelligence, from prioritization and proof of concept through production and adoption. Drives AI transformation, evaluates emerging tools, builds an AI-first culture, manages technical and business stakeholders, communicates AI concepts to senior leaders, and ensures rigorous measurement and attribution. Requires strong AI/ML knowledge, hands-on Python and data ecosystem experience, and leadership of complex cross-functional projects.

Lead Kraken Pro’s social media strategy across X, Telegram, and other platforms. Create platform-native content, react quickly to crypto and trading trends, manage the content calendar, engage the community, translate product updates into compelling narratives, track performance, and optimize through experimentation. Partner with Legal and Communications to ensure accurate, compliant messaging across regions.

Lead and scale an AI-driven creative design team producing static performance ads, photorealistic AI visuals, and video source assets. Own creative direction, production workflows, capacity planning, quality control, testing, and performance-driven iteration. Mentor designers, evaluate emerging AI tools, develop prompt and style libraries, and partner with engineering on creative automation. Collaborate with marketers, media buyers, producers, copywriters, and video editors to deliver high-quality, conversion-focused campaigns.

Build and lead n8n’s internal AI and automation enablement function. Run recurring workshops, office hours, build-alongs, bounty hunts, hackathons, and dogfooding initiatives for technical and non-technical teams. Develop reusable templates, measure AI fluency and adoption, support cross-functional champions, and act as product owner for a safe internal automation playground. Partner with IT, Security, Data, and Solutions Engineering to define access, credential, and environment requirements.

Build AI-powered learning resources, automated workflows, onboarding experiences, and knowledge systems. The role involves prototyping Claude-supported solutions, maintaining Sana LMS and Notion, automating enrollment and reporting, creating learning content, and partnering with stakeholders to solve learning needs in a remote, async-first environment.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Supports Nokia’s Digital Office transformation initiatives by mapping processes, identifying AI and automation opportunities, co-creating requirements and prototypes, building workflows with Microsoft and ServiceNow tools, and developing dashboards and reports. The trainee also supports responsible AI adoption, governance, communications, training, change management, and impact measurement while collaborating with global teams.

Evaluate AI-generated customer service and technical responses for clarity, usefulness, natural language, technical accuracy, JSON validity, formatting, and guideline adherence. Provide clear feedback explaining evaluation decisions. The role requires multilingual fluency, coding knowledge, strong written communication, attention to detail, and consistent judgment. Experience with AI evaluation, quality assurance, APIs, structured data, customer service, software development, or technical writing is preferred.

Lead adoption of Sovos’s Core and AI Platform capabilities across engineering teams. Embed with teams to understand codebases, remove adoption blockers, contribute code using AI-assisted development, and create reusable guidance and automation. Establish standards for AI coding assistants, prompt engineering, code quality, and workflow integration. Collaborate on testing and deployments while addressing security and architectural considerations. Mentor engineers, document best practices, and communicate effectively with technical and non-technical stakeholders.

Lead Chainguard’s European government affairs and public policy strategy across the UK and Europe. Engage regulators, governments, standards bodies, and industry coalitions; develop policy positions and consultation responses; translate cybersecurity regulations into product, positioning, and customer implications; and establish a scalable policy function. The role requires senior stakeholder engagement, technical policy communication, frequent regional travel, and eventual team-building responsibility.

Join a cross-functional Scrum team to embed quality throughout the software lifecycle. Design and maintain Python and pytest automation aligned with CI/CD, apply AI and LLM techniques for test generation and defect analysis, and perform root-cause troubleshooting. Participate in Agile ceremonies, requirements analysis, and reviews while collaborating with developers, architects, and testers. The role supports recent graduates through mentorship and hands-on experience in automated testing, AI quality frameworks, telecommunications, and optical networking.

Design risk-based test strategies and build scalable automated coverage using Playwright, AI coding agents, and CI/CD pipelines. Develop agentic testing workflows, reusable automation frameworks, and AI-powered quality tools. Partner with cross-functional engineering squads to prevent defects, improve observability and release confidence, reduce flakiness, and increase automation coverage. Track quality metrics while enabling developers to integrate reliable automated testing into daily development workflows.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.

Develop and maintain NLP and generative AI products, including RAG systems, content generation, and recommendation applications. Responsibilities include dataset selection and annotation, rapid prototyping, experimentation, statistical evaluation, production deployment, and iterative system improvement. The role requires staying current with AI advances, communicating technical concepts to stakeholders, and collaborating across international teams while addressing multilingual challenges.

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.

Design, implement, and optimize high-performance attention and foundational AI kernels for GPUs, NPUs, ASICs, and other accelerators. Build reusable attention-framework abstractions, profile workloads, develop benchmarks and correctness tests, analyze hardware and compiler behavior, and improve production inference performance. Collaborate with hardware and software teams, document designs, resolve ambiguous performance issues, and mentor engineers while supporting new accelerator architectures and AI workloads.