Applied ML

Role Overview

We are seeking a highly skilled Applied ML engineer to design, build, and operationalize machine learning systems that power data-driven products and decision-making. This role focuses on building robust, production-ready ML solutions with strong emphasis on feature engineering, model performance, human-in-the-loop systems, and long-term model stability.

The ideal candidate combines deep expertise in classical machine learning techniques, strong experimentation and evaluation skills, and hands-on experience deploying models in real-world production environments. You will work closely with data engineers, product teams, and domain experts to translate complex business problems into scalable ML solutions.

Key Responsibilities

• Design and implement end-to-end machine learning pipelines, from data preparation and feature engineering to model deployment and monitoring.
• Develop and optimize supervised learning models using advanced feature engineering techniques.
• Build human-in-the-loop training and validation systems that incorporate user feedback to continuously improve model performance.
• Design evaluation frameworks to measure model performance using advanced metrics and validation strategies.
• Tune and calibrate models using precision/recall trade-offs, threshold optimization, and model performance monitoring.
• Improve model reliability, stability, and robustness through systematic experimentation and error analysis.
• Work closely with data engineering teams to build scalable data pipelines and feature stores for ML applications.
• Collaborate with product and domain teams to translate business requirements into production-grade ML systems.
• Continuously evaluate new tools, frameworks, and techniques to improve ML system performance and operational efficiency.

Required Qualifications

• Strong experience implementing classical machine learning algorithms and supervised learning models.
• Deep expertise in feature engineering and data preparation techniques.
• Strong programming skills in Python and experience with ML libraries such as scikit-learn, XGBoost, LightGBM, or similar frameworks.
• Experience designing model evaluation frameworks and performance metrics.
• Familiarity with ML lifecycle management, including training, validation, deployment, and monitoring.

Preferred Qualifications

• Experience implementing human-in-the-loop machine learning systems and feedback-driven model improvement.
• Experience with model calibration, threshold tuning, and performance optimization for real-world applications.
• Experience deploying ML models into production environments using modern ML infrastructure.
• Familiarity with MLOps practices, including model versioning, monitoring, and retraining pipelines.
• Experience working with large-scale datasets and distributed data processing environments.

Please reach out to us.