Data Integration and Machine Learning Engineer

About the role

This role is central to the company's AI initiatives. It ensures that data from many systems is accurate, complete and usable, integrates it with internal and external systems, and builds production-ready machine learning solutions that deliver measurable business value.

Key responsibilities

• Design and implement AI solutions integrated with ERP, cloud, ECM, IoT and DCS systems
• Build and maintain APIs and data pipelines for reliable AI and ML adoption
• Prepare enterprise data for AI agents using RAG, embeddings and vector databases
• Embed security by design across data integration, AI and ML activities
• Establish a scalable data mesh architecture with metadata, lineage and documentation
• Partner with IT and business teams to define ML problems, baselines, metrics and validation
• Develop and deploy ML models for forecasting, optimization and other business use cases
• Apply statistics and feature engineering with leakage-free validation and explainability

Required profile

• 3 to 5 years in data engineering, ML or integration roles, ideally in manufacturing or ERP-driven industries
• Hands-on experience with ERP, cloud, ECM or similar enterprise applications and production data pipelines
• End-to-end experience developing, deploying and monitoring ML solutions
• Experience with forecasting, optimization or recommendation systems is an advantage

Required skills

• Languages and tools: SQL, Python, PL/SQL, Apache Airflow, Talend, REST APIs
• ML and statistics: scikit-learn, pandas, XGBoost or similar; regression, classification, clustering, time-series forecasting, anomaly detection, model evaluation, optimization with OR-Tools, Pyomo or similar
• Systems and databases: Oracle, PostgreSQL, AWS/Azure Data Lake, NoSQL
• Data management: data cataloging, validation frameworks, observability tools