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Job Summary
We are seeking a hands-on Machine Learning Engineer to own and evolve production-grade machine learning systems that support large-scale operational workflows. This role focuses on developing and maintaining models used to predict time and cost for complex processes. The engineer will work at the intersection of machine learning and data engineering to improve existing models, build new models, and ensure continued accuracy and reliability as data and business conditions evolve.

Key Responsibilities
• Take full ownership of existing machine learning models in production.
• Retrain, tune, and optimize models using new and evolving datasets.
• Build new models for structured and tabular prediction problems.
• Compare multiple models and model versions to determine production readiness.
• Apply appropriate evaluation metrics aligned with business outcomes.
• Identify model drift and implement strategies to maintain model performance.
• Develop and maintain scalable data pipelines for data ingestion, transformation, and feature engineering.
• Work with large-scale datasets using PySpark or similar distributed data processing frameworks.
• Ensure consistency between training and production data.
• Partner with cross-functional teams to understand requirements and deliver machine learning solutions.
• Mentor junior engineers and contribute to best practices in machine learning development.
• Develop, debug, validate, and deploy production-ready machine learning solutions.
• Support the complete machine learning lifecycle from data preparation and model development through evaluation, deployment, and ongoing optimization.

Required Qualifications
• 5+ years of hands-on experience in Machine Learning or Applied Machine Learning Engineering.
• Strong Python programming skills with production-level experience.
• Proven experience building and maintaining machine learning models in production environments.
• Expertise in scikit-learn and classical machine learning techniques for tabular data.
• Experience with PySpark or distributed data processing frameworks.
• Strong SQL and data handling skills.
• Solid understanding of the end-to-end machine learning lifecycle, including data preparation, model development, evaluation, deployment, and monitoring.
• Ability to compare, validate, and select models for production environments.
• Strong debugging and problem-solving skills.
• Experience working with large, complex, and evolving datasets.
• Ability to independently take machine learning solutions from data through production deployment and ongoing maintenance.

Preferred Qualifications
• Experience with gradient boosting models such as CatBoost, XGBoost, or LightGBM.
• Exposure to AWS-based data and machine learning ecosystems.
• Familiarity with MLOps practices, including model versioning, pipelines, deployment, and monitoring.
• Experience with local LLMs or applied Generative AI use cases.
• Knowledge of neural networks.
• Experience mentoring junior engineers and contributing to machine learning engineering best practices.
• Practical approach to machine learning development with a focus on measurable business impact.