Top Artificial Intelligence Jobs in Portugal

The AI/ML Engineer will design and deploy AI pipelines, integrating LLMs into production, and develop scalable machine learning systems while collaborating with stakeholders and data engineers.

Design and optimize compact AI models for resource-constrained devices, implement quantization/pruning/distillation, deploy and tune models on edge hardware (e.g., Jetson, Raspberry Pi), develop efficient on-device analytics, and write high-performance low-level code.

Drive technical excellence in AI projects, design scalable solutions, mentor junior engineers, and align technical roadmaps with business goals.

Lead the design, governance, and implementation of data science and AI architecture, ensuring scalable MLOps integration and collaboration with data engineering teams.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

Lead product strategy and execution for AI-enabled insurance products. Define 6–12 month direction, own P&L-impacting programmes, embed experimentation and AI playbooks, mentor PMs, and partner across engineering, data science, design, legal, and leadership to deliver measurable commercial outcomes.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

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 product direction and roadmaps for major AI Neobank app areas. Own prioritization, execution, launch quality, and outcomes across multiple squads. Guide PMs, designers, and engineers; translate regulatory and business constraints into product decisions; drive post-launch learning and continuous improvement.

Own and deliver product areas within BJAK's AI Neobank app (insurance, payments, savings, investing, travel). Define user problems, scope, metrics, roadmaps, and launch plans. Work cross-functionally with engineering, design, data, operations, compliance and business teams. Launch features, monitor performance, iterate using data and user feedback, and balance speed, UX, business impact and compliance.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

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.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

Join the AI engineering team to develop reliable AI infrastructure for manufacturing systems, focusing on MCP servers, telemetry, and model retraining pipelines.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Lead modernization of month-end and year-end close processes using AI and automation. Prepare, review, and validate journal entries, accruals, and financial statements. Implement anomaly detection, automated reconciliations, variance analysis, and predictive insights. Collaborate with data teams to structure finance data for ML use cases and design audit-ready, scalable reporting workflows.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Manage and optimize end-to-end account reconciliations, intercompany eliminations, and chart-of-accounts governance. Identify discrepancies, perform variance analysis, and implement AI-driven automation and anomaly detection to reduce manual effort and accelerate close. Collaborate with cross-functional teams to resolve mismatches and improve financial data quality and reporting accuracy.