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I am a Machine Learning Engineer with over 3 years of experience, specializing in building and deploying ML models across healthcare, finance, and manufacturing sectors. I am skilled in designing real-time data pipelines and automating model retraining using advanced MLOps tools including AWS SageMaker, Kubernetes, and MLflow. I enjoy leveraging explainable AI techniques such as SHAP and LIME to make models more transparent and trustworthy. My hands-on projects range from predictive maintenance and fraud detection to credit risk scoring and demand forecasting, all aimed at delivering tangible business impact. I thrive when collaborating with both engineering and business teams to translate complex data challenges into actionable solutions that drive efficiency and innovation.…I am a Machine Learning Engineer with over 3 years of experience, specializing in building and deploying ML models across healthcare, finance, and manufacturing sectors. I am skilled in designing real-time data pipelines and automating model retraining using advanced MLOps tools including AWS SageMaker, Kubernetes, and MLflow. I enjoy leveraging explainable AI techniques such as SHAP and LIME to make models more transparent and trustworthy. My hands-on projects range from predictive maintenance and fraud detection to credit risk scoring and demand forecasting, all aimed at delivering tangible business impact. I thrive when collaborating with both engineering and business teams to translate complex data challenges into actionable solutions that drive efficiency and innovation.

Venkata Upendar Reddy Kamasani

AI Engineer, Data Scientist, Developer, +2

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I am a Machine Learning Engineer with over 3 years of experience, specializing in building and deploying ML models across healthcare, finance, and manufacturing sectors. I am skilled in designing real-time data pipelines and automating model retraining using advanced MLOps tools including AWS SageMaker, Kubernetes, and MLflow. I enjoy leveraging explainable AI techniques such as SHAP and LIME to make models more transparent and trustworthy. My hands-on projects range from predictive maintenance and fraud detection to credit risk scoring and demand forecasting, all aimed at delivering tangible business impact. I thrive when collaborating with both engineering and business teams to translate complex data challenges into actionable solutions that drive efficiency and innovation.…I am a Machine Learning Engineer with over 3 years of experience, specializing in building and deploying ML models across healthcare, finance, and manufacturing sectors. I am skilled in designing real-time data pipelines and automating model retraining using advanced MLOps tools including AWS SageMaker, Kubernetes, and MLflow. I enjoy leveraging explainable AI techniques such as SHAP and LIME to make models more transparent and trustworthy. My hands-on projects range from predictive maintenance and fraud detection to credit risk scoring and demand forecasting, all aimed at delivering tangible business impact. I thrive when collaborating with both engineering and business teams to translate complex data challenges into actionable solutions that drive efficiency and innovation.

Available to hire

I am a Machine Learning Engineer with over 3 years of experience, specializing in building and deploying ML models across healthcare, finance, and manufacturing sectors. I am skilled in designing real-time data pipelines and automating model retraining using advanced MLOps tools including AWS SageMaker, Kubernetes, and MLflow. I enjoy leveraging explainable AI techniques such as SHAP and LIME to make models more transparent and trustworthy.

My hands-on projects range from predictive maintenance and fraud detection to credit risk scoring and demand forecasting, all aimed at delivering tangible business impact. I thrive when collaborating with both engineering and business teams to translate complex data challenges into actionable solutions that drive efficiency and innovation.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

Machine Learning Engineer at Illinois Tool Works (ITW)

January 1, 2025 - Present

Contributed to predictive maintenance pilots using sensor data to flag abnormal patterns supporting preventive maintenance decisions. Set up ML pipelines on Kubernetes and AWS SageMaker, reducing manual retraining and testing effort by approximately 30%. Partnered with plant engineers to trial anomaly detection in SCADA/PLC systems for early fault identification. Applied SHAP and LIME to inspection models to improve interpretability for engineers. Assisted in deploying Tableau and Power BI dashboards to enhance real-time production performance visibility.

Machine Learning Engineer at Advocate Aurora Health

December 31, 2024 - August 22, 2025

Supported healthcare ML models including fraud detection with about 88% precision and patient risk stratification improving high-risk patient identification. Built real-time ingestion and ETL workflows using Kafka and Airflow across SQL and NoSQL sources, decreasing pipeline refresh time from daily to hourly and reducing manual preparation by around 25%. Collaborated with DevOps to implement CI/CD pipelines for model retraining, speeding update cycles and minimizing manual deployments.

Machine Learning Engineer at Morgan Stanley

July 31, 2023 - August 22, 2025

Partnered with risk analysts to enhance credit risk scoring models, improving decision consistency and decreasing manual reviews by roughly 10%. Contributed to real-time fraud detection workflows using Kafka and Flink, reducing detection lag from minutes to seconds. Automated ML release processes using Jenkins and GitLab CI, cutting deployment steps from hours to under 30 minutes. Containerized ML workflows with Docker and Kubernetes to improve scalability. Supported customer segmentation modeling to inform targeted marketing campaigns.

Data Scientist at Johnson & Johnson

May 31, 2022 - August 22, 2025

Applied ARIMA and Prophet forecasting models to improve inventory planning accuracy by approximately 12%. Developed analytics pipelines using Spark and Kafka for near real-time supply chain event monitoring. Deployed lightweight ML microservices assisting pharmacovigilance teams in adverse event monitoring. Consolidated R&D, clinical, and commercial datasets into Snowflake, reducing reporting turnaround time from days to hours. Supported statistical validation for FDA submission datasets ensuring regulatory compliance.