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I'm a Machine Learning Engineer with nearly four years of hands-on experience building predictive models that power automotive, retail, and fintech applications. I enjoy turning data into actionable insights and scalable solutions that solve real business problems. I specialize in Python, SQL, TensorFlow, PyTorch, Scikit-learn, and cloud platforms like AWS and Azure. My work has delivered measurable results—23% improvement in predictive maintenance accuracy, 17% uplift in marketing ROI, and 40% reduction in retraining cycles through NLP (BERT, GPT), deep learning (CNN, LSTM), and MLOps practices (Airflow, MLflow, Docker, FastAPI). I also focus on explainable AI using SHAP and LIME to support compliant, enterprise-wide adoption.…I'm a Machine Learning Engineer with nearly four years of hands-on experience building predictive models that power automotive, retail, and fintech applications. I enjoy turning data into actionable insights and scalable solutions that solve real business problems. I specialize in Python, SQL, TensorFlow, PyTorch, Scikit-learn, and cloud platforms like AWS and Azure. My work has delivered measurable results—23% improvement in predictive maintenance accuracy, 17% uplift in marketing ROI, and 40% reduction in retraining cycles through NLP (BERT, GPT), deep learning (CNN, LSTM), and MLOps practices (Airflow, MLflow, Docker, FastAPI). I also focus on explainable AI using SHAP and LIME to support compliant, enterprise-wide adoption.

Prashanth Kedri

AI Engineer, Data Scientist, Developer, +3

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I'm a Machine Learning Engineer with nearly four years of hands-on experience building predictive models that power automotive, retail, and fintech applications. I enjoy turning data into actionable insights and scalable solutions that solve real business problems. I specialize in Python, SQL, TensorFlow, PyTorch, Scikit-learn, and cloud platforms like AWS and Azure. My work has delivered measurable results—23% improvement in predictive maintenance accuracy, 17% uplift in marketing ROI, and 40% reduction in retraining cycles through NLP (BERT, GPT), deep learning (CNN, LSTM), and MLOps practices (Airflow, MLflow, Docker, FastAPI). I also focus on explainable AI using SHAP and LIME to support compliant, enterprise-wide adoption.…I'm a Machine Learning Engineer with nearly four years of hands-on experience building predictive models that power automotive, retail, and fintech applications. I enjoy turning data into actionable insights and scalable solutions that solve real business problems. I specialize in Python, SQL, TensorFlow, PyTorch, Scikit-learn, and cloud platforms like AWS and Azure. My work has delivered measurable results—23% improvement in predictive maintenance accuracy, 17% uplift in marketing ROI, and 40% reduction in retraining cycles through NLP (BERT, GPT), deep learning (CNN, LSTM), and MLOps practices (Airflow, MLflow, Docker, FastAPI). I also focus on explainable AI using SHAP and LIME to support compliant, enterprise-wide adoption.

Available to hire

I’m a Machine Learning Engineer with nearly four years of hands-on experience building predictive models that power automotive, retail, and fintech applications. I enjoy turning data into actionable insights and scalable solutions that solve real business problems.

I specialize in Python, SQL, TensorFlow, PyTorch, Scikit-learn, and cloud platforms like AWS and Azure. My work has delivered measurable results—23% improvement in predictive maintenance accuracy, 17% uplift in marketing ROI, and 40% reduction in retraining cycles through NLP (BERT, GPT), deep learning (CNN, LSTM), and MLOps practices (Airflow, MLflow, Docker, FastAPI). I also focus on explainable AI using SHAP and LIME to support compliant, enterprise-wide adoption.

Skills

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Expert

Expert

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Intermediate

Work Experience

Machine Learning Engineer at General Motors

February 1, 2025 - Present

At General Motors, I enhanced predictive maintenance accuracy by 23% using regression and classification models deployed in AWS SageMaker, significantly reducing unplanned downtime and warranty claims. I increased sentiment classification precision to 91% via BERT NLP pipelines, which shortened customer service resolution times by 18%. Optimized CNN-based defect detection models to reduce false positives by 28%, accelerating inspections across production facilities. I achieved 40% faster retraining cycles by automating ML pipelines with Airflow and tracking experiments in MLflow. Delivered high-performance inference services containerized with Docker and FastAPI, handling over 1 million daily API requests with sub-200ms latency. Enabled real-time edge defect detection saving $2M in quality control costs. Integrated explainability tools like SHAP and LIME to reduce manual compliance reviews by 60%, boosting cross-team adoption by 45% through BI tool integrations and monitoring model hea

Machine Learning Engineer at Cybage Software

May 1, 2023 - September 4, 2025

At Cybage Software, I improved targeted marketing campaign ROI by 17% through advanced customer segmentation using K-Means and DBSCAN models, generating $1.2M incremental revenue. Developed LSTM time-series models that boosted demand forecast accuracy by 19%, reducing inventory overstock costs by 22%. Accelerated deployment by building Flask APIs hosted on AWS EC2, reducing timeline by 35%. Created executive dashboards in Tableau and Power BI that cut reporting workload by 45% and enabled faster leadership decisions. Ensured regulatory compliance via rigorous model validation metrics. Decreased drift incidents by 30% through continuous monitoring and retraining. Improved data preprocessing speeds by 25%, enhancing model training efficiency. Integrated ML insights into CRM platforms resulting in 12% higher user engagement and optimized AWS compute resources to lower infrastructure costs by 20%.

Machine Learning Engineer at General Motors

February 1, 2025 - November 6, 2025

Implemented regression and classification models in AWS SageMaker to improve predictive maintenance accuracy by 23%, reducing downtime and warranty claims by $4.5M annually. Developed BERT-based sentiment analysis improving customer service resolution times by 18% across 1.2M calls. Optimized CNN-based defect detection across 12 facilities, reducing false positives by 28%. Automated ML pipelines with Airflow and MLflow to achieve 40% faster retraining. Deployed inference services with Docker and FastAPI achieving sub-200ms latency for 1M+ daily requests. Enabled edge defect detection with TensorFlow Lite, saving $2M. Introduced SHAP/LIME explainability reducing compliance review hours by 60% and monitored model drift to boost reliability by 35%. Integrated ML outputs into BI tools increasing cross-team adoption by 45%.

Machine Learning Engineer at Cybage Software

May 1, 2023 - May 1, 2023

Delivered K-Means/DBSCAN segmentation increasing targeted campaign ROI by 17% (about $1.2M incremental revenue). Improved demand forecast accuracy by 19% with LSTM time-series models, reducing overstock and carrying costs by 22%. Cut deployment time by 35% by building Flask APIs on AWS EC2. Reduced reporting workload by 45% via Tableau/Power BI dashboards. Validated ML outputs with ROC-AUC, F1, and precision-recall; implemented continuous monitoring to cut drift incidents by 30%. Accelerated data preprocessing by 25% using optimized Pandas/Numpy pipelines. Increased client CRM engagement by 12% through ML-driven personalization. Reduced infrastructure costs by tuning AWS resources.