Principal Machine Learning Engineer[W2 ROLE]

Medical Guardian is seeking a Principal Machine Learning Engineer to lead the design, development, deployment, and optimization of machine learning solutions supporting predictive analytics, scoring, decision intelligence, and AI-driven automation. This is a hands-on technical leadership role focused on building production-ready ML models while partnering with stakeholders to solve complex business problems. Key Responsibilities

• Design, build, validate, and deploy machine learning models for prediction, scoring, risk detection, prioritization, and decision support.
• Perform exploratory data analysis (EDA), feature engineering, model training, tuning, validation, and performance evaluation.
• Develop scalable ML pipelines using Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, and similar technologies.
• Build reusable feature engineering frameworks and model-ready datasets.
• Monitor production models for performance, drift, calibration, retraining, and lifecycle management.
• Collaborate with business and technical stakeholders to translate business challenges into ML solutions.
• Ensure models are explainable, maintainable, and production-ready.
• Follow software engineering best practices including version control, testing, documentation, and code quality.
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• 5+ years of hands-on experience in machine learning model development.
• 3+ years of experience deploying and supporting machine learning models in production environments.
• Strong programming experience with Python and SQL.
• Experience with Databricks, Apache Spark, MLflow, Snowflake, Azure, AWS, or similar cloud/data platforms.
• Strong understanding of feature engineering, model evaluation, model monitoring, drift detection, calibration, thresholding, and retraining.
• Experience developing predictive models, scorecards, and decision-support systems.
• Ability to communicate technical concepts effectively to both technical and non-technical stakeholders.
• Strong software engineering practices including testing, documentation, reproducibility, and maintainable code.

Preferred Qualifications

• Experience with Generative AI and AI automation.
• Knowledge of MLOps and production machine learning lifecycle management.
• Experience with explainable AI (XAI) and transparent modeling techniques.
• Background in predictive analytics, customer engagement, or risk modeling.
• Experience working in Agile environments., * Machine Learning
• Python
• SQL
• Databricks
• Apache Spark
• MLflow
• scikit-learn
• XGBoost
• Snowflake
• Azure / AWS
• Feature Engineering
• Predictive Modeling
• Model Deployment
• MLOps
• Generative AI
• AI Automation
• Model Monitoring
• Stakeholder Management

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