Open role, R&D
R&D Gen AI
ML Engineer
Research that has to ship. Generative AI, LLMs, micro-LLMs and agents, taken from a working experiment to something a client will put their name on.
Model behaviour, agents and the road to a product
This is the seat where new model behaviour gets explored and then made real. Generative AI, LLMs, micro-LLMs and agent systems, applied to marketing and to the industries our clients work in, with the ambition of building owned intellectual property rather than assembling someone else’s.
The distinction that matters here is between a demo and a product. Plenty of teams can get a model to do something impressive once. This role is for someone who can then make it reliable, affordable to run, and legible enough that a client will sign off on it. That means prompt and context architecture, evaluation you can defend, and knowing when a smaller model or a plain piece of code beats the interesting answer.
On the stack, we are deliberate rather than dogmatic. Google Cloud is our preferred cloud, and it is where most of what we build lives. On models we stay agnostic by design, with a clear preference for three families: Anthropic, Google Gemini and OpenAI. Knowing where the frontier is, from Mythos to Gemini Enterprise to Astra, matters less as trivia than as judgement about which model belongs in which step, and at what cost.
A large part of the craft is the modelling itself: designing a generative AI solution as a system rather than a single call. Chaining models, routing between them, letting a small one do what a large one is wasted on, and connecting the whole thing to agentic no-code or low-code automation where that is the sensible answer rather than writing everything from scratch.
It is a research seat with a delivery obligation, inside an R&D team that sits close to the client work rather than apart from it. Nothing here stops at a proof of concept, and nothing here is judged by a slide.
What you will actually do
- Research & ImplementationResearch, design, and implement machine learning and deep learning systems and workflows, focusing on generative AI and large language models, in ecosystems such as marketing, customer engagement, customer experience and sales.
- End-to-End DeliveryDevelop and operationalize AI solutions, from data collection and processing to model deployment and monitoring.
- Productization FrameworksCreate and maintain scalable frameworks for the productization of AI-based software, ensuring performance and efficiency.
- Systematic ExperimentationBrainstorm and run experiments across data feeds, software integrations, digital assistants, NLP applications, journey hyper-personalization, sentiment analysis, predictive analytics, computer vision, recommendation systems, predictive lead scoring, multivariate testing and LLM-powered assistants.
- Cost & PerformanceOwn the economics of what you build: latency, token cost and the model choice that makes a solution viable at client scale rather than only at demo scale.
- Cross-Functional IntegrationCollaborate with engineering, marketing and product teams to integrate AI solutions into commercial products, with a market-first approach and a data-centric view of iterative failure as part of the process.
- EvaluationConduct experiments and tests with AI models, optimizing them based on performance analysis, and build the evaluation sets that make a claim of improvement mean something.
- Documentation & Knowledge SharingDocument frameworks, algorithms, journeys and custom innovations to ensure reproducibility and knowledge sharing within the team and with partner innovation hubs, including R&D centres and universities.
- IP & ComplianceBuild owned innovation solutions and methodologies that comply with ethical guidelines, data privacy laws and regulations, and best industry standards.
Technical & platform skills
- Experience
- 1 to 3 years building with machine learning or generative AI, in industry, in research, or in projects substantial enough to talk through in detail.
- Programming
- Proficiency in Python or similar, and proven experience developing and deploying machine learning solutions in experimentation or production.
- Generative AI
- Mid to deep knowledge of generative AI, LLMs and frameworks such as TensorFlow, PyTorch and LangChain, with JAX, Keras, FastAI, Transformers or DeepSpeed as a plus.
- Data engineering
- Experience with data modeling and transforming unstructured data into usable formats: cleaning, preprocessing, feature engineering, analytics engineering and ETL, with scikit-learn, pandas or Spark MLlib.
- Cloud AI platforms
- Knowledge or experience with Vertex AI, Generative AI Studio, Model Garden, NotebookLM, Gemini, Generative AI App Builder, Azure Databricks or Azure Cognitive Services.
- MLOps
- Knowledge or experience with MLOps tooling such as MLflow, Kubeflow, Amazon SageMaker, DVC or TFX, and with software engineering practice: version control, testing and continuous integration.
- Low-code judgement
- Experience with low-code and no-code ecosystems, and the architectural judgement to know when custom code is not the right answer.
- Model evaluation
- Comfort evaluating model performance with accuracy, precision, recall, F1, log loss, MSE and AUC, and knowing which of them matters for a given problem.
- Education
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics or a related field, preferred.
- Solution modelling
- The ability to design a generative AI solution as a system: several models chained or routed against each other, each chosen for the step it suits, with agentic no-code or low-code automation wired in where it earns its place.
- Model landscape
- Working familiarity with the Anthropic, Google Gemini and OpenAI families, and enough sense of where the frontier sits, Mythos, Gemini Enterprise, Astra, to judge what is worth adopting and what is worth waiting out.
- AI literacy
- Literacy in Tech and AI subjects, and practical understanding of how LLMs and agents work: context, tools, memory, and why the same prompt can return different answers. Sophisticated use of AI platforms, both with efficiency and security.
Behavioral skills
- Clear Communication
- Strong written and verbal communication in Portuguese and English, to collaborate with different teams and stakeholders.
- Problem Building
- Analytical problem-building and problem-solving in mid to complex challenges, including a critical mindset about data sets, evolutions and patterns.
- Beginner’s Mindset
- Able to iterate at different speeds, from rigorous experiment to pure growth hacking, with escalation ambition.
- Business Bridge
- Bridge with operational teams and clients, understanding technical requirements and bottlenecks while listening for business needs and market opportunities.
- Kindness
- At Wise Pirates we promote and admire kindness, independently of rhythm, intensity and efficiency.
- Collaborative Spirit
- Readiness to work with kind and collaborative people, where egos stay behind, with flexibility and autonomy but rigor and responsibility.
- Innovation Lover
- Desire to evolve technically and professionally in a scale-up environment, with ambition and hunger for innovation.
- Results-Oriented Zeal
- An unwavering commitment to the client’s bottom line, pushing boundaries to chase the best overall performance and business goals.
Not required. Noticed.
- Experience within the Adtech or Martech sector, or software engineering applied to services and innovation.
- Knowledge of cost and performance optimization techniques in LLM-based solutions.
- AI/ML certifications on Google Cloud or Microsoft Azure.
- Experience with MLOps and model deployment infrastructure.
- Contributions to open-source ML projects, or publications in relevant conferences and journals.
- Knowledge of agile methodologies and experience in collaborative, diverse work environments.
- Postgraduate studies in Data Science.
- Additional years of experience beyond the range above, for more ownership over what we research and build.
- Data pipeline and MLOps depth, which is the neighbouring seat’s core and a strong complement here.
A Digital, Tech and AI Business Partner
Wise Pirates is a leading Digital, Tech, and AI Business Partner, operating from Portugal. With a team of 100+ professionals, we are a Martech-independent agency at the forefront of innovation in Southern Europe. Our ambition is to lead an AI-first marketing ecosystem, blending human creativity with the power of artificial intelligence.
We are dynamic, forward-thinking, and driven by data, automation, and meaningful customer experiences. We believe that our people are our most valuable asset, and we are committed to fostering a culture where they can grow, innovate, and thrive.
Our value proposition is holistic and new generation. We combine services with proprietary products, custom data and proprietary frameworks, so the client gets one capability aimed at their business results, not a stack of separate disciplines. You get to work across all of it.
The part most
job ads skip
- A front-row seat in a growing agency placing AI at the centre of how we work and how we serve our clients, both in service and products. Senior peers in every discipline, our sharpest edge and your fastest growth.
- A culture of autonomy connected to a clear orientation towards action and performance. You own your projects end-to-end and have real space to grow. The rhythm is intense and evolution happens at a fast pace.
- Direct exposure to a wide range of use cases, industries, and clients across 80+ countries, with real impact on what gets shipped.
- Continuous learning alongside a cross-functional team of automation, data, media, and creative specialists.
Why join the navy when you can be a pirate?
Portugal, hybrid, full time. Direct client impact from day one, alongside senior peers in every discipline.
Apply for this role
