Top Tech Jobs & Startup Jobs in Portugal

Lead an engineering team building endpoint configuration management capabilities, including configuration policies, baselines, drift detection, remediation, staged rollouts, reporting, and audit trails. Own delivery, architecture, production operations, automated deployment pipelines, and cross-functional alignment with product and core engineering teams. Guide technical decisions and apply AI technologies where beneficial.

Research, fine-tune, train, and deploy large language models for real-time commentary and interactive avatar applications. Build RAG systems, agentic workflows, multimodal solutions, and production-grade inference services with monitoring and tool integration. Collaborate with ML engineers, product developers, and domain experts to create reliable, observable AI systems.

Sources, engages, screens, and qualifies candidates across European markets, with a focus on the DACH region. Builds talent pipelines, conducts market mapping and benchmarking, delivers candidate shortlists, and advises hiring managers on talent availability, salary benchmarks, and competitor landscapes. Partners with recruiters and business stakeholders while managing multiple hiring priorities and ensuring a strong candidate experience.

Manage full-cycle accounting, reporting, tax compliance, treasury, and financial analysis for Portuguese SPVs. Responsibilities include preparing financial statements under Portuguese GAAP and IFRS, handling VAT and corporate tax filings, managing cash flows and reconciliations, supporting audits and transactions, and improving financial controls. The role collaborates with legal, tax, investment, auditors, and regulatory stakeholders in a hybrid renewable energy environment.

Own end-to-end global customer implementation projects from kickoff through go-live and handover. Build project plans, establish governance, manage RAID logs, milestones, dependencies, risks, and escalations, and coordinate clients and internal teams across countries and time zones. Lead status and governance meetings, drive accountability, ensure go-live readiness, maintain documentation, and support continuous process improvement.

Own customer success, experience, renewal, and expansion for high-value Salesforce customers. Build relationships with business and technical stakeholders, align platform value with customer goals, monitor account health, create success plans, manage major incident communications, and collaborate with Sales, Engineering, and Product. The role requires Portuguese and English fluency, strong consulting and stakeholder-influence skills, and the ability to translate technical concepts into business terms.

Build and maintain Databricks data pipelines, contribute to enterprise data architecture and modeling, develop tested transformation logic, and deliver reliable datasets for BI and analytics. Optimize workflows for scalability, reliability, and cost efficiency while establishing data engineering standards and documentation. Collaborate with BI, data, IT, and business teams to support modern data practices and future AI use cases.

Leads and develops a distributed engineering team through performance reviews, coaching, mentoring, feedback, and career planning. Manages cross-functional squad collaboration, facilitates retrospectives and check-ins, represents engineering in organizational discussions, and promotes accountability and continuous improvement. Provides technical oversight across client projects, identifies knowledge gaps, guides training, and supports engineering best practices, particularly in WordPress and emerging technologies.

Analyzes complex and raw data from multiple sources, identifies trends and patterns, evaluates business needs, and develops analytical tools, pipelines, algorithms, and prototypes. The role prepares data for predictive and prescriptive modeling, improves data quality, supports data architects, and collaborates with data scientists to deliver actionable insights.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.