Data Integration and Machine Learning Engineer

This role plays a vital part in driving the success of our AI initiatives by ensuring the quality and availability of enterprise data, accurate problem formulation, and dependable model training. The Integration and Machine Learning Engineer will be responsible for managing data from multiple systems to guarantee it is accurate, complete, and usable. This position involves integrating data with both internal and external systems and developing production-ready machine learning solutions that generate measurable business value.

Key Responsibilities:
- Design and implement AI solutions that integrate 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 techniques such as Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
- Embed security by design throughout data integration, AI, and ML activities to ensure robust protection.
- Establish scalable data mesh architecture, including metadata management, data lineage, and comprehensive 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 focused on forecasting, optimization, and other key business use cases.
- Apply statistical methods and feature engineering with leakage-free validation and model explainability to ensure trustworthy outputs.

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, ECM, or similar enterprise applications, along with production data pipelines.
- Demonstrated ability to develop, deploy, and monitor end-to-end machine learning solutions.
- Experience with forecasting, optimization, or recommendation systems is highly desirable.

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, and techniques such as regression, classification, clustering, time-series forecasting, anomaly detection, and model evaluation. Experience with optimization frameworks like OR-Tools or Pyomo is a plus.
- Knowledge of systems and databases including Oracle, PostgreSQL, AWS or Azure Data Lake, and NoSQL technologies.
- Experience with data management practices such as data cataloging, validation frameworks, and observability tools to ensure data quality and system reliability.

This position offers the opportunity to contribute directly to impactful AI and ML projects within a dynamic environment, working alongside cross-functional teams to transform enterprise data into actionable insights and business value.