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Hi, I’m Sudheer Kumar Reddy Batthina, an AI and machine learning engineer based in Buffalo, NY. I design and deploy scalable AI solutions, predictive models, and deep learning systems across Azure, AWS, and Databricks, turning complex data into practical business outcomes. With 4+ years of hands-on experience, I build end-to-end ML pipelines, apply MLOps practices for automated training and deployment, and craft explainable AI solutions that meet governance and transparency standards. I enjoy turning data-driven insights into measurable improvements in efficiency, reliability, and decision-making.…Hi, I’m Sudheer Kumar Reddy Batthina, an AI and machine learning engineer based in Buffalo, NY. I design and deploy scalable AI solutions, predictive models, and deep learning systems across Azure, AWS, and Databricks, turning complex data into practical business outcomes. With 4+ years of hands-on experience, I build end-to-end ML pipelines, apply MLOps practices for automated training and deployment, and craft explainable AI solutions that meet governance and transparency standards. I enjoy turning data-driven insights into measurable improvements in efficiency, reliability, and decision-making.

Sudheer Kumar Reddy Batthina

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

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Hi, I’m Sudheer Kumar Reddy Batthina, an AI and machine learning engineer based in Buffalo, NY. I design and deploy scalable AI solutions, predictive models, and deep learning systems across Azure, AWS, and Databricks, turning complex data into practical business outcomes. With 4+ years of hands-on experience, I build end-to-end ML pipelines, apply MLOps practices for automated training and deployment, and craft explainable AI solutions that meet governance and transparency standards. I enjoy turning data-driven insights into measurable improvements in efficiency, reliability, and decision-making.…Hi, I’m Sudheer Kumar Reddy Batthina, an AI and machine learning engineer based in Buffalo, NY. I design and deploy scalable AI solutions, predictive models, and deep learning systems across Azure, AWS, and Databricks, turning complex data into practical business outcomes. With 4+ years of hands-on experience, I build end-to-end ML pipelines, apply MLOps practices for automated training and deployment, and craft explainable AI solutions that meet governance and transparency standards. I enjoy turning data-driven insights into measurable improvements in efficiency, reliability, and decision-making.

Available to hire

Hi, I’m Sudheer Kumar Reddy Batthina, an AI and machine learning engineer based in Buffalo, NY. I design and deploy scalable AI solutions, predictive models, and deep learning systems across Azure, AWS, and Databricks, turning complex data into practical business outcomes.

With 4+ years of hands-on experience, I build end-to-end ML pipelines, apply MLOps practices for automated training and deployment, and craft explainable AI solutions that meet governance and transparency standards. I enjoy turning data-driven insights into measurable improvements in efficiency, reliability, and decision-making.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

Graduate Student Assistant at University at Buffalo

January 31, 2025 - January 31, 2025

Built TensorFlow forecasting models to predict lab equipment usage and streamline resource allocation, leading to a 28% drop in idle time and improved scheduling efficiency. Automated research paper classification using BERT and SpaCy NLP models, cutting manual indexing time by 70% and improving cross-department data accessibility. Deployed scalable inference microservices on Azure Kubernetes with FastAPI, enabling real-time analytics dashboards that enhanced lab utilization tracking. Integrated MLflow and Airflow for experiment tracking and retraining automation, improving model reproducibility and reducing maintenance effort by 40%. Incorporated SHAP and LIME frameworks to generate interpretable model insights, strengthening trust and transparency during academic peer reviews. Streamlined deployment workflows using Docker and Azure DevOps, cutting production release time by half and ensuring consistency across environments.

MLOps Engineer at Wipro Limited

March 31, 2024 - March 31, 2024

Developed scalable machine learning pipelines in Azure Databricks to process continuous telemetry streams from 20K+ IoT devices, enabling near real-time analytics reducing latency by 35% across the ingestion layer. Built forecasting models using Prophet and XGBoost to predict compute resource needs and energy utilization, allowing data teams to make proactive scaling decisions that cut cloud overspending by 24%. Created LSTM-driven predictive maintenance systems that analyzed sensor signals for anomaly detection and failure prediction, helping operations teams reduce unplanned downtime by 30% and extend asset life cycles. Deployed containerized FastAPI services on Azure Kubernetes Service (AKS) to deliver low-latency inference, ensuring 99% uptime and real-time visibility into IoT device health across the production ecosystem. Implemented a robust MLOps workflow integrating MLflow for tracking and Airflow for automation, standardizing retraining and deployment pipelines that accelerate

AI & Machine Learning Engineer at Wipro Limited

September 30, 2022 - September 30, 2022

Built and fine-tuned anomaly detection models using Random Forest, Gradient Boosting, and SVM in Azure Databricks to identify irregular server behavior, leading to a 20% decrease in unplanned downtime incidents across production systems. Transformed raw infrastructure telemetry into structured datasets with PySpark pipelines, enabling faster model training and boosting data throughput by 30% for large-scale analytics workloads. Developed Delta Lake–based ETL frameworks to ensure version-controlled, ACID-compliant storage, resulting in more reliable analytics and consistent data availability for downstream ML pipelines. Integrated MLflow tracking with Azure DevOps CI/CD pipelines to automate model versioning, validation, and deployment, which reduced manual release effort by 50% while maintaining deployment accuracy. Delivered real-time monitoring dashboards in Power BI that visualized anomaly scores, latency metrics, and alert trends, helping IT operations cut mean resolution time by