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I am an AI/ML Engineer with over three years of experience delivering end-to-end machine learning solutions in healthcare and automotive domains. I specialize in predictive analytics, anomaly detection, and NLP, and I build robust ETL pipelines with Apache Airflow and SQL. I deploy scalable models on AWS SageMaker, TensorFlow, and Docker, and I create insights-driven dashboards with Tableau and Power BI. I am a collaborative team player who thrives in Agile environments, and I prioritise data privacy and HIPAA compliance, ensuring robust security throughout the ML lifecycle.…I am an AI/ML Engineer with over three years of experience delivering end-to-end machine learning solutions in healthcare and automotive domains. I specialize in predictive analytics, anomaly detection, and NLP, and I build robust ETL pipelines with Apache Airflow and SQL. I deploy scalable models on AWS SageMaker, TensorFlow, and Docker, and I create insights-driven dashboards with Tableau and Power BI. I am a collaborative team player who thrives in Agile environments, and I prioritise data privacy and HIPAA compliance, ensuring robust security throughout the ML lifecycle.

Mrunmayi Aashish Saoji

Data Scientist, AI Engineer, Full Stack Developer, +2

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I am an AI/ML Engineer with over three years of experience delivering end-to-end machine learning solutions in healthcare and automotive domains. I specialize in predictive analytics, anomaly detection, and NLP, and I build robust ETL pipelines with Apache Airflow and SQL. I deploy scalable models on AWS SageMaker, TensorFlow, and Docker, and I create insights-driven dashboards with Tableau and Power BI. I am a collaborative team player who thrives in Agile environments, and I prioritise data privacy and HIPAA compliance, ensuring robust security throughout the ML lifecycle.…I am an AI/ML Engineer with over three years of experience delivering end-to-end machine learning solutions in healthcare and automotive domains. I specialize in predictive analytics, anomaly detection, and NLP, and I build robust ETL pipelines with Apache Airflow and SQL. I deploy scalable models on AWS SageMaker, TensorFlow, and Docker, and I create insights-driven dashboards with Tableau and Power BI. I am a collaborative team player who thrives in Agile environments, and I prioritise data privacy and HIPAA compliance, ensuring robust security throughout the ML lifecycle.

Available to hire

I am an AI/ML Engineer with over three years of experience delivering end-to-end machine learning solutions in healthcare and automotive domains. I specialize in predictive analytics, anomaly detection, and NLP, and I build robust ETL pipelines with Apache Airflow and SQL.
I deploy scalable models on AWS SageMaker, TensorFlow, and Docker, and I create insights-driven dashboards with Tableau and Power BI. I am a collaborative team player who thrives in Agile environments, and I prioritise data privacy and HIPAA compliance, ensuring robust security throughout the ML lifecycle.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at CitiusTech

January 1, 2024 - Present

Developed and deployed predictive analytics models to identify high-risk patients for hospital readmission, improving early intervention strategies. Built and fine-tuned classifiers (Logistic Regression, Random Forest, XGBoost) achieving over 85% accuracy on historical EHR data. Implemented NLP with spaCy and NLTK to extract critical insights from unstructured clinical notes, enhancing feature engineering. Automated ETL pipelines using Apache Airflow and SQL to ingest and preprocess data from multiple sources while maintaining data integrity. Deployed the model on AWS SageMaker with real-time scoring and dashboards in Tableau/Power BI. Ensured HIPAA-compliant data handling with robust security and validation checks.

AI/ML Engineer at Kpit Technologies

June 1, 2022 - October 24, 2025

Designed and implemented a predictive maintenance system for automotive clients to forecast component failures and minimize downtime. Pre-processed large volumes of sensor/telemetry data using Python (Pandas/NumPy). Developed and refined classifiers (Random Forest, XGBoost) achieving up to 15% accuracy improvement over previous checks. Automated model training and deployment pipelines using TensorFlow, Docker, and MLflow, reducing update time by 40%. Visualized anomaly detection and failure predictions with Power BI and Matplotlib. Collaborated in Agile environments with data engineers and domain experts; documented deliverables and created reusable Jupyter Notebooks.