AI Engineer

• Enablement & coaching — work directly with engineers through 1:1s, pair programming, workshops, office hours, and cohorts to raise their effectiveness with AI-assisted development: prompting, workflow design, and model/tool selection. Identify under-adoption and lift teams up to the standard.
• Best-practice propagation — capture what works (and what fails) in one team and systematically transfer it: living best-practice guides, prompt libraries, troubleshooting playbooks, and an internal AI knowledge base and skills registry.
• Scale through champions — stand up and support a network of embedded AI champions so enablement scales past a single person.
• Unblocking & feedback loop — be the go-to when engineers get stuck with AI tooling. Proactively tour teams to surface blockers early, aggregate them into patterns, and escalate systemic issues to leadership.
• Internal integrations & tooling — build internal AI integrations and reusable tooling hands-on (Node.js/TypeScript, MCP) — from prototype to production independently, including auth, access control, security review, and CI/CD.
• Evaluation & scouting — run structured evaluations of AI tools, models, and internal integrations; monitor the fast-moving landscape (Claude Code, Cursor, Copilot/Codex, new model releases) and deliver curated, actionable recommendations.
• Measurement & impact — define and track adoption and productivity metrics by team, tool, and individual. Build dashboards on real usage and output — adoption/active-usage rate, assistant acceptance rate, PR throughput and cycle time — with a quality guardrail (change failure rate) so speed never comes at the cost of quality. Reference frameworks: DX Core 4, DORA.
• Cross-functional partnering — partner with security/compliance on safe-by-design guardrails, and with GTM to translate field needs into internal enablement and feed adoption patterns back.

Senior AI Enablement Engineer

• Build and ship Android features using Kotlin.
• Create clean mobile flows for insurance, payments, claims, renewals and financial services.
• Work with product and design to simplify complex user journeys.
• Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
• Improve app performance, crash rate, loading states, responsiveness and memory usage.
• Use analytics, user behaviour and production issues to improve the product.
• Build AI-assisted mobile experiences only where they genuinely improve the user journey.

Mobile Application Developer – AI Finance Agent

• Design and develop end-to-end NLP pipelines — from classical text processing to state-of-the-art LLM-powered architectures
• Build and maintain systems for intent detection, NER, entity extraction, and text classification, both standalone and as components feeding into larger LLM workflows
• Design and optimize Retrieval-Augmented Generation (RAG) systems — chunking strategies, vector store architecture, hybrid search (dense + sparse), and re-ranking pipelines
• Work with embedding models for semantic search, document retrieval, and intent classification in contact center contexts
• Design and implement agentic architectures — tool use, function calling, multi-step reasoning, and orchestration with frameworks like LangChain, LlamaIndex, or custom-built solutions
• Develop memory and context management strategies — short-term conversation memory, long-term user context, and context window optimization for multi-turn interactions
• Evaluate and benchmark models rigorously: hallucination detection, faithfulness scoring, latency/token cost tradeoffs, and continuous performance monitoring
• Integrate AI components into scalable, production-ready microservices with a focus on low-latency inference pipelines
• Collaborate with product and engineering to design new AI-powered features and drive innovation across the platform

• Develop, refine, and establish best practices for effective prompt engineering across the organization
• Support and coach business users in crafting prompts to maximize AI solution utilization
• Create comprehensive guides, templates, and resources for prompt engineering
• Troubleshoot and optimize prompts for better outcomes
• Provide hands-on technical support to business users implementing AI solutions
• Conduct training sessions on AI tools and prompt best practices
• Help integrate AI tools into business workflows and processes
• Document and share technical learnings and case studies across the organization
• Stay current with AI platforms, tools, and emerging capabilities
• Evaluate new AI solutions for organizational use
• Mentor junior technical staff on AI implementation
• Lead technical communities of practice and knowledge-sharing initiatives

Senior AI Engineer

• Design and build AI solutions using Microsoft Copilot Studio
• Develop and maintain Power Automate flows to automate business processes
• Build and operationalise AI models and pipelines on Azure AI Foundry
• Integrate AI solutions with Nadara's data platforms
• Collaborate with data engineers, domain experts and business stakeholders
• Monitor and maintain deployed AI solutions

• Accelerate AI adoption: identify opportunities to streamline and enhance business processes through AI, advocate for best practices, and support the implementation of AI-driven initiatives.
• Workforce AI solution expertise: serve as a subject matter expert for workforce AI solutions (such as Copilot), guiding colleagues in selecting and using the right tools for their needs.
• Lead AI communities: build and nurture communities of practice and champions networks around artificial intelligence, encouraging knowledge sharing, experimentation, and cross-functional collaboration.
• Presentations & workshops: develop and deliver engaging presentations and hands-on workshops to groups ranging from 1 to 53,000 people, ensuring content is accessible to both technical and non-technical audiences.
• Resource development: create, curate, and disseminate educational materials, playbooks, and resources that empower staff to confidently integrate AI into their workflows.
• Feedback and improvement: gather feedback from colleagues, monitor trends and challenges within the community, and work with technology teams to improve AI tools and resources.

Enterprise AI Enablement Lead

Machine Learning Engineer

• Build core ML systems that power a proactive, long-horizon AI product.
• Own work end-to-end: data preparation, training, evaluation, inference, and iteration.
• Turn research ideas into working systems that run reliably in production.
• Debug model failures and system issues using real production signals.
• Iterate quickly: ship, measure outcomes, refine, and repeat.
• Collaborate closely with research, product, and engineering to deliver real user impact.
• Mentor and review work from other ML engineers through example and technical judgment.
• Work under real production constraints: latency, cost, reliability, and safety

Senior Machine Learning Engineer

• Build and improve ML components across data, training, evaluation, and inference.
• Fine-tune and adapt models as part of larger production systems.
• Implement evaluation and testing to understand model behavior.
• Help build and maintain data pipelines for real-world and synthetic data.
• Debug model issues, performance problems, and production incidents.
• Ship improvements iteratively and learn from real user feedback.
• Work closely with senior ML engineers and product teams.
• Work under real production constraints: latency, cost, reliability, and safety

Member of Technical Staff, Machine Learning

• Build customer-facing and internal product features end to end.
• Develop web flows, backend APIs, admin tools, dashboards and operational systems.
• Support products across insurance, payments, savings, investing, travel and financial services.
• Work with product, design, mobile and operations teams to launch fast.
• Integrate APIs, handle data flows and improve production reliability.
• Use analytics, feedback and production issues to improve features after launch.
• Help build tools that reduce manual work and improve speed across the business.

Full Stack Software Engineer – AI Neobank App

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems
• Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows
• Create AI-assisted experiences for various operational tasks
• Work with product and engineering teams to build and improve solutions
• Design around real constraints

Applied AI Engineer

• Build AI-powered workflows, assistants, agents and automation systems.
• Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools.
• Work with product and engineering teams to turn manual processes into scalable AI-native systems.
• Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems.
• Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable.
• Prototype quickly, test with users or operators, then productionize what works.
• Improve speed, quality and consistency across workflows using AI where it creates real business value.

Applied AI Engineer – AI Neobank App

• Develop and implement Generative AI/LLM solutions.
• Collaborate with cross-functional teams for project success.
• Ensure robust integration with Microsoft 365 services.
• Handle intelligent document processing tasks.

AI Engineer, KYC

Mobile Developer – AI Neobank App

• As a Principal ML Architect, you will be the senior technical leader for our most ambitious client projects. You will act as a trusted advisor to clients, a mentor to your engineering team, and the chief architect for complex AI systems. This is a hands-on leadership role that combines deep technical expertise with strategic client management.
• Lead Technical Strategy & Architecture: Partner with clients to understand their core business challenges and design sophisticated, scalable solutions leveraging machine learning, search, and agentic AI paradigms.
• Manage Client Engagements: Serve as the primary technical point of contact for client stakeholders, translating complex concepts, managing expectations, and ensuring project success from a technical and business perspective.
• Lead & Mentor Engineering Teams: Guide a team of talented ML engineers, fostering a culture of excellence, providing technical direction, and taking ultimate responsibility for project delivery.
• Drive Innovation: Move beyond conventional ML to architect and implement cutting-edge agentic workflows, agent-based modeling, multi-agent systems, and complex RAG pipelines that deliver transformative value.
• Oversee the Full Project Lifecycle: Steer projects from initial discovery and architectural design through to deployment, monitoring, and iteration, ensuring solutions are robust, efficient, and production-ready.

Principal AI/ML Architect

• Design, train, and evaluate machine learning models to address specific business problems.
• Build and maintain data pipelines and infrastructure to support model development and deployment.
• Deploy ML models into production and monitor their performance, reliability, and drift over time.
• Identify and resolve technical issues, bugs, and blockers as they arise during development and deployment.
• Work closely with team members across various departments to understand their data, processes, and needs, and adapt solutions accordingly.
• Iterate on deployed models to keep them accurate, efficient, and useful as needs evolve.

• Lead the strategic direction of high-value pursuits, RFPs, and business proposals
• Craft compelling narratives that clearly communicate business value, product vision, and AI-driven innovation
• Translate complex technology and AI capabilities into client-focused solutions and business outcomes
• Collaborate with product leaders, designers, architects, and delivery teams to shape differentiated client proposals
• Apply product thinking and entrepreneurial mindset to uncover opportunities before clients fully define their needs
• Build reusable proposal frameworks, storytelling assets, and strategic positioning materials
• Leverage AI tools throughout the proposal lifecycle to accelerate research, synthesis, and content creation

Product Strategy Lead, AI & Growth

• Designing, building, and maintaining scalable data pipelines and ETL/ELT workflows supporting ML and GenAI use cases using Python, and AWS services.
• Building analytics tools that use the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
• Managing data ingestion, orchestration, transformation, quality, and curation at scale.
• Implementing and maintaining systems via event-driven architectures and APIs.
• Collaborating closely with Data Scientists, product teams, and business stakeholders to translate data needs into robust solutions.
• Ensuring high engineering standards: unit testing, mocking, CI/CD, code quality, repository structure, environment setup.
• Providing technical guidance, code reviews, and mentoring to team members.

• Ship, don't specify
• Own a product problem end to end: from framing to prototype to production.
• Move in short cycles. Days and weeks, not quarters. Your output is working software in users' hands, not decks.
• Own the feedback loop. Get in front of customers, learn, and ship the next version fast.
• Agentic coding and rapid prototyping are your default, not a side experiment.
• You're a multiplier: you ship at a pace a conventional team can't match.
• You'll help shape how we build with AI. That's a happy side effect, not the mission. The product is.
• Our stack is TypeScript, React, Node, and AWS. We care more about how fast you ramp than what you know today.
• You own the quality bar and the judgement behind what you build, not just the output.
• Make product, design, and scope calls without waiting for a committee, and own them when you're wrong.
• When the brief is incomplete, you narrow it yourself and move.
• Your name's on what ships. You're measured on impact, not tickets closed.

AI Product Engineer

• Serve as a key bridge between digital, creative, and content processes and their automated execution using AI across the entire global Devoteam group.
• Design, document, and implement AI-driven marketing workflows, fostering experimentation, and accelerating the adoption of innovative technologies.
• Design, build, and optimize automated marketing workflows using low-code/no-code platforms like Make.com or n8n.
• Integrate and leverage Large Language Models (LLMs) such as Gemini and Claude to automate content creation and operational processes.
• Manage, synchronize, and track marketing data across core platforms, including HubSpot, Salesforce, and corporate social media channels.
• Execute and support hands-on creative tasks, including digital copywriting, basic editing, and minor graphic design.
• Lead continuous experimentation with emerging AI technologies, tools, and frameworks to keep marketing operations at the vanguard.
• Drive "Change Adoption" by mentoring, training, and guiding internal marketing teams to integrate AI into their daily routines.
• Collaborate closely with the Group’s Brand & Content Director to ensure strategic alignment, brand consistency, and high visibility of innovation initiatives.
• Design and maintain data dashboards to track process efficiency, performance, and the ROI of implemented automations.

AI & Innovation Marketer / Marketing AI Lead

• Develop and scale enterprise web applications using Angular
• Integrate frontend applications with Python/FastAPI backends
• Implement GPT/LLM-based modules to support evaluation, automation, and workflow optimisation
• Design, build, and consume REST APIs within a microservices architecture
• Implement secure authentication and authorisation mechanisms
• Collaborate closely with Data Science and platform teams to integrate AI capabilities
• Participate in technical architecture discussions and contribute to system design decisions
• Ensure scalability, maintainability, and performance of applications
• Contribute to Agile ceremonies and delivery planning
• Support deployment, integration, and release activities across environments

Machine Learning Engineer jobs in Portugal

See all jobs on JobTailor

Senior AI Enablement Engineer

Mobile Application Developer – AI Finance Agent

AI Engineer

Senior AI Engineer

AI Engineer

Enterprise AI Enablement Lead

Senior Machine Learning Engineer

Member of Technical Staff, Machine Learning

Full Stack Software Engineer – AI Neobank App

Applied AI Engineer

Applied AI Engineer – AI Neobank App

AI Engineer, KYC

Mobile Developer – AI Neobank App

Principal AI/ML Architect

Machine Learning Engineer

Product Strategy Lead, AI & Growth

Machine Learning Engineer

AI Product Engineer

AI & Innovation Marketer / Marketing AI Lead

Senior AI Engineer – German Speaker