QDM Engineer - AI/ML Model

Role Overview :

The QDM Engineer (AI/ML) will bridge the gap between automotive manufacturing/quality processes and advanced data analytics. You will build and maintain data infrastructure, connect plant floor systems (PLCs, sensors, MES, ERP), and develop machine learning models and automated pipelines to optimize product quality, defect detection, and operational efficiency.

Key Responsibilities :

• AI/ML Model Development : Design, train, and deploy machine learning and statistical models for automotive quality control, predictive analytics, and anomaly detection.

• Data Infrastructure & Integration : Build and maintain scalable data pipelines to collect, store, process, and visualize manufacturing and plant data.

• OT/IT Convergence : Connect shop-floor hardware, PLCs, industrial sensors, MES (Manufacturing Execution Systems), ERPs, and quality systems into a unified analytics platform.

• Exploratory Data Analysis (EDA) : Perform deep-dive data analysis on vehicle/component quality metrics, supplier performance, and line-reject trends.

• Data Visualization & Reporting : Build interactive dashboards and visual reports using tools like Tableau, Matplotlib, or Seaborn for technical and non-technical stakeholders.

• Governance & Security : Ensure data security, proper access controls, regular backups, and compliance with data integrity protocols.

• Cross-Functional Collaboration : Translate complex technical/ML concepts into actionable insights for plant managers, quality assurance, and engineering teams.

Essential Skills & Qualifications :

• Experience : 3 to 6 years of hands-on experience in Data Analysis and Machine Learning (ideally within an automotive, manufacturing, or industrial background).

• Programming Languages : Proficiency in Python and core libraries (pandas, scikit-learn, TensorFlow, PyTorch).

• Data Management : Strong familiarity with SQL and database management systems.

• Statistical & ML Knowledge : Solid grounding in statistical analysis, regression, classification, and time-series/predictive algorithms.

• Visualization & BI : Experience with tools such as Tableau, PowerBI, Matplotlib, or Seaborn.

• Industrial & Domain Familiarity : Exposure to plant data systems, PLCs, sensors, MES, ERP, and quality management frameworks (e.g., Six Sigma, SPC).

• Productivity Tools : Basic to intermediate proficiency in MS Office suite (Excel, PowerPoint, Word).

• Soft Skills : Strong problem-solving mindset, attention to detail, and ability to present technical data clearly to leadership.