PURPOSE \& OVERALL RELEVANCE FOR THE ORGANIZATION A Machine Learning Engineer applies foundational knowledge of the end\-to\-end Model Development Lifecycle (MDLC), software engineering, cloud technologies, and modern AI methodologies to help build, deploy, and scale machine learning solutions. They collaborate with cross\-functional teams to transform proofs\-of\-concept into reliable and scalable production systems — with growing focus on Generative AI and agentic AI frameworks. KEY RESPONSIBILITIES MACHINE LEARNING ENGINEERING * Support the design and development of ML components for data and ML infrastructure (data pipelines, feature stores, model training/inference services) * Assist in implementing end\-to\-end ML pipelines (MLOps), including data ingestion, feature engineering, training, deployment, and model monitoring * Work with data scientists to productionize models and ensure business value is consistently delivered * Contribute to model observability — logging, drift tracking, performance dashboards GENAI \& AGENTIC AI * Use LLMs, prompt engineering, embeddings, and vector stores to enable intelligent applications * Build small\-scale AI agents using frameworks like LangChain, LlamaIndex, or equivalent * Experiment with responsible and explainable use of foundation models to solve clear business problems ANALYTICS * Assist in applying machine learning techniques with guidance from senior engineers or data scientists.Perform exploratory data analysis and support feature selection and data preparation * Use unsupervised learning when appropriate for early insights or pattern discovery DATA MANAGEMENT \& ENGINEERING * Support creation, improvement, and validation of curated datasets for ML applications * Contribute to data quality checks, schema design, and efficient feature retrieval * Follow best practices for security, accessibility, and ethical use of data. PROGRAMMING / SOFTWARE DEVELOPMENT * Write clean, reliable, well\-tested code (primarily in Python) * Implement and maintain CI/CD workflows for ML components with supervision * Deploy ML workloads on cloud or on\-prem environments using modern tooling. VISUALIZATION \& STORYTELLING * Build automated dashboards to support model/data health visibility * Communicate insights clearly to technical and non\-technical stakeholders. TESTING \& RELIABILITY * Contribute to writing unit, integration, and regression tests for ML components * Monitor test outcomes and support issue resolution. EDUCATION \& EXPERIENCE — MINIMUM QUALIFICATIONS * 1\+ years experience in a Machine Learning, Data Engineering, or AI\-focused software engineering role (internships and academic projects count) * Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (Master’s not required) * Solid understanding of Python, data structures, and basic software engineering practices * Familiarity with: + ML frameworks: scikit\-learn, TensorFlow, or PyTorch + GenAI / Agentic frameworks: LangChain, LlamaIndex, Hugging Face, vector databases (e.g., FAISS, Pinecone) + MLOps concepts: model packaging, CI/CD, containerization (Docker), REST/Batch inference * Some exposure (academic or project\-based) to cloud platforms (AWS, Azure, GCP) and distributed data tools (Spark, Kafka) is a plus * Interest in modern AI topics such as prompt engineering, embeddings, and responsible AI. SOFT SKILLS * Clear and concise verbal and written communication (English) * Collaborative mindset and willingness to learn from peers * Ability to break down complex problems and take initiative on tasks * Resilient, detail\-oriented, and passionate about emerging AI technologies. AT ADIDAS WE HAVE A WINNING CULTURE. BUT TO WIN, PHYSICAL POWER IS NOT ENOUGH. JUST LIKE ATHLETES OUR EMPLOYEES NEED MENTAL STRENGTH IN THEIR GAME. WE FOSTER THE ATHLETE’S MINDSET THROUGH A SET OF BEHAVIORS THAT WE WANT TO ENABLE AND DEVELOP IN OUR PEOPLE AND THAT ARE AT THE CORE OF OUR UNIQUE COMPANY CULTURE: THIS IS HOW WE WIN WHILE PLAYING FAIR. COURAGE: Speak up when you see an opportunity; step up when you see a need.. OWNERSHIP: Pick up the ball. Be proactive, take responsibility and follow\-through. INNOVATION: Elevate to win. Be curious, test and learn new and better ways of doing things. TEAMPLAY: Win together. Work collaboratively and cultivate a shared mindset. INTEGRITY: Play by the rules. Hold yourself and others accountable to our company’s standards. RESPECT: Value all players. Display empathy, be inclusive and show dignity to all. **AT ADIDAS, WE STRONGLY BELIEVE THAT EMBEDDING DIVERSITY, EQUITY, AND INCLUSION (DEI) INTO OUR CULTURE AND TALENT PROCESSES GIVES OUR EMPLOYEES A SENSE OF BELONGING AND OUR BRAND A REAL COMPETITIVE ADVANTAGE.** **– CULTURE STARTS WITH PEOPLE, IT STARTS WITH YOU –** **BY RECRUITING TALENT AND DEVELOPING OUR PEOPLE TO REFLECT THE RICH DIVERSITY OF OUR CONSUMERS AND COMMUNITIES, WE FOSTER A CULTURE OF INCLUSION THAT ENGAGES OUR EMPLOYEES AND AUTHENTICALLY CONNECTS OUR BRAND WITH OUR CONSUMERS.** JOB TITLE: Machine Learning Analyst BRAND: LOCATION: Porto TEAM: Data STATE: 13 COUNTRY/REGION: PT CONTRACT TYPE: Full time NUMBER: 536709 DATE: Nov 13, 2025