Chenthil Lingamurthy

Background

About Chenthil Lingamurthy

Highly accomplished MLOps and AI professional with 16+ years of experience in Linux distributions, including RHEL, CentOS, Ubuntu, Debian, and SLES, across on-premises and cloud environments. Proven expertise in designing, deploying, and managing large-scale AI and machine learning (ML) workloads on Linux, with a strong focus on MLOps, DevOps, and cloud computing. MLOps Expertise- Designed and implemented MLOps pipelines using popular frameworks like TensorFlow, PyTorch, and Scikit-Learn - Deployed and managed ML models on cloud platforms like Azure, AWS, and Google Cloud - Implemented automated ML workflows using tools like Ansible, Terraform, and Kubernetes - Collaborated with data scientists to develop and deploy AI/ML models, ensuring seamless integration with existing infrastructure AI and Machine Learning Experience- Worked with various AI and ML frameworks, including TensorFlow, PyTorch, and Scikit-Learn - Developed and deployed deep learning models for computer vision, natural language processing, and predictive analytics - Implemented data preprocessing, feature engineering, and model training using popular libraries like Pandas, NumPy, and Matplotlib - Tuned and optimized AI/ML models for performance, scalability, and reliability Linux and Cloud Expertise- 16+ years of experience with Linux distributions, including RHEL, CentOS, Ubuntu, Debian, and SLES - Strong hands-on expertise in Linux internals, debugging, diagnostics, and capacity planning - Experience with public clouds like Azure, AWS, and Google Cloud, including migration and management of workloads - Implemented infrastructure-as-code solutions using Ansible, Terraform, and Kubernetes Certifications and Awards- Certified Azure Professional - Certified AWS Professional - Certified Kubernetes Administrator (CKA)- RHCE Expert Key Skills- MLOps, DevOps, and cloud computing - AI and machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn)- Linux distributions (RHEL, CentOS, Ubuntu, Debian, SLES)- Cloud platforms (Azure, AWS, Google Cloud)- Automation tools (Ansible, Terraform, Kubernetes)- Data preprocessing, feature engineering, and model training - Deep learning models for computer vision, NLP, and predictive analytics