Description

You will design, develop, and deploy machine learning models and GenAI-driven workflows to solve complex engineering and operational problems.

This role is on-site.

Responsibilities

• Build and tune predictive models using XGBoost, Random Forest, and scikit-learn, evaluating performance via MAE, RMSE, and R².
• Embed ML models into GenAI multi-agent workflows, implementing RAG pipelines for document intelligence and contextual Q&A.
• Analyze structured and unstructured datasets from SAP/EES, historical project attributes, and regulatory documentation to perform feature engineering.
• Support model lifecycle management including versioning, reproducibility, and monitoring for drift, bias, and performance degradation.
• Provide human-readable explanations for model predictions and recommendations to stakeholders.

Required Skills

• 5+ years of experience in Data Science, Machine Learning, and Statistics.
• Proficiency in Python, including NumPy, Pandas, scikit-learn, and XGBoost.
• Hands-on experience building regression, calibration, and classification models.
• Strong understanding of model evaluation metrics and error distribution analysis.
• Experience working with large, complex enterprise datasets and ensuring data quality.
• Bachelor's degree in a quantitative field or equivalent professional experience.

Preferred Skills

• Exposure to GenAI/LLM systems, RAG pipelines, and vector search concepts.
• Familiarity with computer vision outputs, image classification, and non-text data analysis.
• Experience in utilities, infrastructure, or regulated industries with a focus on AI governance and explainability.

Key Skills

Education

Bachelor's degree