Machine Learning Engineer Intern
10 hours ago
Madrid
WHAT YOU WILL BE DOING You will join Getnet's AI Lab, driving the design, development, and production deployment of machine learning models and data-driven solutions across multiple geographies. You will work end-to-end: from exploratory analysis and model training to production-grade pipelines and monitoring. The role will be responsible for design, train, and deploy all kinds of ML models on Getnet data at scale. Build and maintain end-to-end ML pipelines from feature engineering through scoring and monitoring. Act as technical lead in the design and execution of proofs of concept and new AI/ML initiatives within Getnet Payments' AI Lab. Drive technical decision-making and collaborate with business and product teams to identify high-impact opportunities. Furthermore, contribute to code quality, documentation, and team best practices. Mentor junior team members and communicate technical results to non-technical stakeholders. WHAT WE ARE LOOKING FOR Skills & Knowledge: ~ Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative field (PhD is a plus). Strong foundations in statistics, probability, and linear algebra applied to real-world ML problems. ~5+ years of professional experience in Machine Learning Engineering, Data Science, or Applied Research roles with a proven track record of deploying ML models to production in cloud environments. Background in fintech, payments, banking, or e-commerce is highly valued. ~ Strong proficiency in Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM, SciPy, statsmodels) with solid experience building and deploying classification, regression, uplift, and anomaly detection models on tabular data at scale. Knowledge of feature engineering, temporal validation, explainability (XAI), and mathematical optimization. ~ Experience with observational causal inference (propensity scores, IPW, AIPW, meta-learners, Causal Forests), uplift modeling, A/B testing, and experimental design. Familiarity with libraries such as EconML, causalml, and DoWhy. ~ Proven experience deploying and operating ML models on Databricks (Workflows, Delta Lake, Unity Catalog, Databricks Connect) within Azure cloud (Azure Databricks, Azure OpenAI Service, AKS, Key Vault). Experience with PySpark/Spark SQL and MLflow end-to-end (experiment tracking, model registry, serving, versioning). ~ Experience with anomaly detection, change point detection, time series clustering, seasonality/trend analysis, and production model monitoring (drift detection, data quality alerts, performance dashboards). ~ Understanding of transformer-based architectures and LLM concepts. Familiarity with prompt engineering, integration of LLMs into ML pipelines, and operationalization of LLM-based solutions via Azure OpenAI or similar services. ~ Ability to write clean, testable, and well-documented Python code. Experience with Git, CI/CD for ML, automated testing (pytest), and schema validation. Experience working in agile, cross-functional teams across multiple markets or geographies. ~(Nice to have): Experience designing and deploying AI agents based on LLMs (LangChain, LangGraph, OpenAI Agents SDK, CrewAI, Agno). Familiarity with MCP, agent observability (LangFuse, MLflow Tracing), agentic patterns (multi-agent orchestration, RAG, tool use), and domain-specific (vertical) AI models for industry use cases. ~(Nice to have): Experience with AWS (SageMaker, S3, Bedrock, EKS, Lambda, Step Functions, Glue) and ability to work across multi-cloud environments (Azure + AWS).
