ph3About The Role /h3 pThis role combines data platform engineering and MLOps responsibilities, supporting both reliable analytical data flows and production-ready machine learning solutions. The person will maintain and improve ETL pipelines, support the data platform environment, and help deploy, monitor, and optimize machine learning models in production. The role requires close cooperation with Data Science, Engineering, BI, Risk, and IT teams to ensure that data and model processes are scalable, well-documented, stable, and aligned with business needs. /p h3Key Responsibilities /h3 ul liDeploy machine learning models into production environments and support their operational lifecycle. /li liSupport cloud-based analytical, reporting, and machine learning infrastructure. /li liCollaborate closely with Data Science, Engineering, Risk, BI, and IT teams to align data and model requirements with production standards. /li liDevelop automation for model deployment, updates, scaling, and recurring data processing tasks. /li liImplement monitoring for both data pipelines and machine learning models, including performance, availability, and quality checks. /li liEnsure reliable operation and continuous development of the analytical data warehouse environment. /li liDesign, maintain, troubleshoot, and optimize ETL/data pipelines supporting reporting, analytics, and machine learning use cases. /li liEnsure timely and high-quality data availability for BI, Risk, Data Science, and other business stakeholders. /li liIdentify, investigate, and resolve performance issues across data warehouse, ETL, and model deployment processes. /li liTroubleshoot, debug, upgrade,



and improve existing software, pipelines, and deployment processes. /li liGather and evaluate user feedback, recommend improvements, and execute enhancements. /li liMaintain technical documentation for data processes, model deployments, configurations, and operational procedures. /li /ul h3Qualifications And Experience /h3 ul li2+ years of experience in data engineering and machine learning /li liStrong Python programming skills and intermediate SQL knowledge /li liGood understanding of databases, data warehouse concepts, and ETL processes /li liUnderstanding of machine learning lifecycle and model operationalization /li liUsing LLM’s to generate and optimize code, ability to use AI platform features to enhance and speed up workflows /li liKnowledge of DevOps practices, CI/CD pipelines, and version control /li liExperience with cloud-based analytical and reporting solutions, preferably Azure /li liFamiliarity with machine learning frameworks and tools such as scikit-learn and XGBoost /li liFamiliarity with containerization technologies such as Docker /li liAbility to monitor, troubleshoot, and optimize data pipelines, infrastructure, and deployed models /li liExperience with software design, development, debugging, and documentation /li liProficient English, B1/B2 level or higher. /li /ul h3What We Offer /h3 ul liA friendly and collaborative team culture /li liThe opportunity to learn from experienced colleagues and grow within IT /li liA modern technical environment with room for improvement and innovation /li liA workplace where teamwork, curiosity, and continuous improvement are valued /li /ul /p #J-18808-Ljbffr

📌 Machine Learning Ops and Data Engineer (Málaga)
🏢 TF Bank AB (publ)
📍 Málaga