AI/ML Engineer

Experience Level : 4–6 Years

• Employment Type : Full-Time
• Department : AI Platform / Digital Experience Innovation
• Core Tech Stack : Python, XGBoost, Random Forest, Ensemble Methods, Oracle ERP, SQL

Key Responsibilities

• Pipeline Development : Design, build, and maintain robust, automated multi-model machine learning pipelines (including XGBoost, Random Forest, and custom Ensemble architectures) for high-accuracy demand and sales forecasting.
• Granular Modeling : Develop and fine-tune predictive models at both macro-levels (PPGC) and highly granular levels (SKU) to support localized and global business planning.
• Data Engineering & Preprocessing : Architect data preprocessing workflows, handling missing values, scaling, feature engineering, and outlier detection from enterprise data sources.
• System Integration : Collaborate with IT and enterprise data teams to orchestrate seamless data integration pipelines between the ML forecasting platform and

Oracle ERP systems.

• Accuracy & Validation : Continuously measure, track, and optimize model performance using robust metrics, specifically targeting

MAPE (Mean Absolute Percentage Error) and minimizing forecast bias.

• Workflow Design : Conceptualize and implement Business Unit (BU) override workflow systems, enabling domain experts to review, validate, and inject manual adjustments safely into automated model outputs.

Required Skills & Qualifications

• Experience : 4–6 years of professional experience as an AI/ML Engineer, Data Scientist, or Predictive Analytics Engineer with a primary focus on time-series forecasting or demand planning.
• Core ML Expertise : Advanced, hands-on proficiency in building tree-based models (

XGBoost ,

Random Forest ) and implementing

Ensemble methods .

• Programming : Mastery of

Python and its scientific/ML ecosystem (e.g., Pandas, NumPy, Scikit-Learn, Statsmodels).

• Enterprise ERP Knowledge : Proven track record of integrating machine learning models or pipeline outputs with

Oracle ERP or similar enterprise-scale resource planning software.

• Domain Knowledge : Solid understanding of supply chain, inventory metrics, SKU management, and corporate sales cycles.
• Education : Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Operations Research, or a related quantitative field.

Preferred Skills

• Exposure to MLOps frameworks for pipeline automation, model deployment, and version control (e.g., MLflow, Kubeflow, Git).
• Strong SQL skills for querying massive, complex relational databases.
• Experience working in high-tech manufacturing, semiconductor, or complex hardware supply chain environments.