ML Engineer (Classical ML)

• Design and develop machine learning models for predictive analytics and optimization across EPC lifecycle

• Translate engineering and operational problems into ML solutions such as cost forecasting, schedule risk, and equipment reliability

• Build end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and monitoring

• Apply statistical techniques and ML algorithms including supervised, unsupervised, time series, and NLP methods

• Optimize model performance, scalability, and reliability for production environments

• Collaborate with data architects to ensure scalable and secure ML architectures

• Communicate insights, assumptions, and limitations clearly to business stakeholders

• Ensure compliance with data governance, security, and privacy standards

• Implement CI/CD and MLOps practices for model deployment and lifecycle management

• Mentor junior engineers and contribute to reusable ML frameworks and standards