Data Integration and Machine Learning Engineer

This role is essential to the success of our AI initiatives, focusing on ensuring the quality and availability of enterprise data, effective problem formulation, and reliable model training. The Integration and Machine Learning Engineer will work to guarantee that data from multiple systems is accurate, complete, and ready for use. This position involves integrating data across internal and external platforms and developing production-grade machine learning solutions that generate tangible 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 to support reliable AI and ML adoption.
- Prepare enterprise data for AI agents using Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
- Embed security by design principles throughout data integration, AI, and ML processes.
- Establish scalable data mesh architecture incorporating metadata management, data lineage, and thorough documentation.
- Collaborate closely with IT and business teams to define machine learning problems, establish baselines, select metrics, and validate models.
- Develop and deploy ML models for forecasting, optimization, and other business-critical use cases.
- Apply statistical methods and feature engineering techniques with leakage-free validation and model explainability.

Required Qualifications:
- 3 to 5 years of experience in data engineering, machine learning, or integration roles, preferably within manufacturing or ERP-driven industries.
- Proven hands-on experience working with ERP, cloud platforms, ECM, or similar enterprise applications and managing production data pipelines.
- Demonstrated end-to-end experience in developing, deploying, and monitoring machine learning solutions.
- Experience with forecasting, optimization, or recommendation systems is considered an advantage.

Preferred Qualifications:
- Proficiency in programming languages and tools such as SQL, Python, PL/SQL, Apache Airflow, Talend, and REST APIs.
- Familiarity with machine learning and statistical libraries including scikit-learn, pandas, XGBoost, or equivalents; expertise in regression, classification, clustering, time-series forecasting, anomaly detection, model evaluation, and optimization using OR-Tools, Pyomo, or similar frameworks.
- Experience working with systems and databases such as Oracle, PostgreSQL, AWS/Azure Data Lake, and NoSQL databases.
- Knowledge of data management practices including data cataloging, validation frameworks, and observability tools.

This position offers the opportunity to contribute directly to impactful AI and ML projects by ensuring data integrity and delivering scalable, secure, and effective machine learning solutions across enterprise systems.