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Hi, I’m Hemanth Kumar Kottapalli, a Machine Learning Engineer based in Atlanta, GA, with 3+ years of experience designing, developing, and deploying ML and DL solutions for healthcare and financial analytics. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face Transformers, and I enjoy building CNNs, RNNs, and Transformers to tackle real-world problems. I excel at data processing with Pandas, NumPy, Spark, and Dask, and I’ve built scalable ML pipelines on AWS SageMaker and Google Cloud AI Platform. I’m comfortable with MLOps using Docker, Kubernetes, and MLflow, and I value clean code, documentation, and insightful visualizations with Tableau and Matplotlib.…Hi, I’m Hemanth Kumar Kottapalli, a Machine Learning Engineer based in Atlanta, GA, with 3+ years of experience designing, developing, and deploying ML and DL solutions for healthcare and financial analytics. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face Transformers, and I enjoy building CNNs, RNNs, and Transformers to tackle real-world problems. I excel at data processing with Pandas, NumPy, Spark, and Dask, and I’ve built scalable ML pipelines on AWS SageMaker and Google Cloud AI Platform. I’m comfortable with MLOps using Docker, Kubernetes, and MLflow, and I value clean code, documentation, and insightful visualizations with Tableau and Matplotlib.

Hemanth Kumar Kottapalli

AI Engineer, Developer, Data Scientist, +5

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Hi, I’m Hemanth Kumar Kottapalli, a Machine Learning Engineer based in Atlanta, GA, with 3+ years of experience designing, developing, and deploying ML and DL solutions for healthcare and financial analytics. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face Transformers, and I enjoy building CNNs, RNNs, and Transformers to tackle real-world problems. I excel at data processing with Pandas, NumPy, Spark, and Dask, and I’ve built scalable ML pipelines on AWS SageMaker and Google Cloud AI Platform. I’m comfortable with MLOps using Docker, Kubernetes, and MLflow, and I value clean code, documentation, and insightful visualizations with Tableau and Matplotlib.…Hi, I’m Hemanth Kumar Kottapalli, a Machine Learning Engineer based in Atlanta, GA, with 3+ years of experience designing, developing, and deploying ML and DL solutions for healthcare and financial analytics. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face Transformers, and I enjoy building CNNs, RNNs, and Transformers to tackle real-world problems. I excel at data processing with Pandas, NumPy, Spark, and Dask, and I’ve built scalable ML pipelines on AWS SageMaker and Google Cloud AI Platform. I’m comfortable with MLOps using Docker, Kubernetes, and MLflow, and I value clean code, documentation, and insightful visualizations with Tableau and Matplotlib.

Available to hire

Hi, I’m Hemanth Kumar Kottapalli, a Machine Learning Engineer based in Atlanta, GA, with 3+ years of experience designing, developing, and deploying ML and DL solutions for healthcare and financial analytics. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face Transformers, and I enjoy building CNNs, RNNs, and Transformers to tackle real-world problems.

I excel at data processing with Pandas, NumPy, Spark, and Dask, and I’ve built scalable ML pipelines on AWS SageMaker and Google Cloud AI Platform. I’m comfortable with MLOps using Docker, Kubernetes, and MLflow, and I value clean code, documentation, and insightful visualizations with Tableau and Matplotlib.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

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Expert

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Expert

Expert

Language

English

Fluent

Work Experience

ML Engineer at McKesson

July 1, 2024 - Present

Developed and deployed predictive models using Python, Scikit-learn, and XGBoost to optimize healthcare supply chain processes, improving inventory forecasting accuracy by 15%. Designed and implemented CNNs with TensorFlow for medical image analysis, enhancing diagnostic support systems for radiology departments. Utilized AWS SageMaker and EC2 for scalable machine learning pipelines, reducing model training time by 20%. Conducted data preprocessing and feature engineering with Pandas and NumPy. Integrated REST APIs with Flask enabling real-time model predictions. Collaborated via Git and GitHub for version control. Created interactive dashboards with Tableau for monitoring model performance.

ML Engineer at Infinite Infolab

July 1, 2023 - August 5, 2025

Built and fine-tuned Transformer-based models using Hugging Face Transformers and PyTorch for NLP tasks, achieving a 12% improvement in sentiment analysis accuracy. Orchestrated Kubernetes and Docker containers to deploy ML models for scalable and reliable production applications. Leveraged Apache Spark and Dask for distributed data processing of terabyte-scale datasets supporting real-time analytics. Implemented CI/CD pipelines with MLflow, reducing deployment time by 25%. Designed RNNs for financial time-series forecasting with Keras. Used Google Cloud AI Platform and BigQuery for large-scale data workflows. Conducted advanced text analysis with SpaCy and NLTK to extract insights from unstructured data. Visualized complex datasets with Seaborn and Matplotlib for decision-making.

ML Engineer at McKesson

July 1, 2024 - November 6, 2025

Developed and deployed predictive models using Python, Scikit-learn, and XGBoost to optimize healthcare supply chain processes, improving inventory forecasting accuracy by 15%. Designed and implemented CNNs with TensorFlow for medical image analysis, enhancing diagnostic support systems for radiology. Built scalable ML pipelines on AWS SageMaker and EC2, reducing model training time by 20%. Performed data preprocessing with Pandas and NumPy; integrated REST APIs with Flask for real-time predictions; collaborated with cross-functional teams using Git/GitHub; visualized model performance with Tableau.

ML Engineer at Infinite Infolab

July 1, 2023 - July 1, 2023

Built and fine-tuned Transformer-based NLP models using Hugging Face Transformers and PyTorch, achieving a 12% improvement in sentiment analysis. Deployed ML models with Kubernetes and Docker for production readiness. Leveraged Apache Spark and Dask for distributed data processing on terabyte-scale datasets to support real-time analytics. Implemented CI/CD pipelines with MLflow, reducing deployment time by 25%. Designed RNNs for time-series forecasting with Keras and utilized Google Cloud Platform’s AI Platform and BigQuery for large-scale model training and evaluation. Performed advanced text analysis with SpaCy and NLTK; visualized results with Seaborn and Matplotlib.