Piloted a major upgrade of the application framework; reduced bugs by 15%.Developed and implemented Kubernetes-based MLOps pipelines for the deployment of machine learning models, ensuring scalable and reliable performance.Designed and automated CI/CD processes using Jenkins and Docker, which resulted in a 25% increase in team productivity.Implemented CI/CD pipelines with Jenkins and GitHub, ensuring efficient collaboration between data science and engineering teams, reducing deployment time by 30%.Developed model versioning and tracking systems using MLFlow, enabling seamless model iteration, experimentation, and deployment.Integrated Azure DevOps with MLOps pipelines to enable continuous integration and continuous delivery of ML models across multiple environments.Collaborated with data scientists to deploy machine learning models in production environments, optimizing for performance, scalability, and cost efficiency using Azure Kubernetes Service (AKS).Monitored deployed models using Prometheus and Grafana, setting up alerts for model drift and performance anomalies.

Built and maintained scalable MLOps pipelines using AWS, Kubernetes, and Jenkins, enabling efficient deployment of microservices-based machine learning models.Automated model deployment processes using Docker and Kubernetes, reducing manual intervention and improving deployment times by 20%.Deployed and monitored machine learning APIs and models in production using AWS SageMaker and Prometheus for real-time monitoring.Collaborated in an Agile environment to manage and maintain CI/CD pipelines, ensuring rapid and error-free model releases.Implemented model version control and reproducibility workflows using MLFlow and Git, ensuring seamless model retraining and updates.

Architected and managed end-to-end MLOps pipelines, automating model deployment using Azure ML and Kubernetes for large-scale machine learning projects.Integrated Google Cloud Pub/Sub for efficient model inference workflows and real-time data processing.Developed and maintained machine learning pipelines using Airflow, Docker, and MLFlow, facilitating automated model retraining and deployment.

Involved in Agile/Scrum software development methodology with developing of large size complex customer centric web application.Spearheaded the automation of build, deployment, and monitoring processes using Jenkins and Docker, reducing manual efforts by 30%.Managed infrastructure using Terraform to deploy scalable and secure environments in AWS.Monitored performance using Prometheus and Grafana, setting up alerts for quick issue resolution.