Feb 2024 – Present · 2 yrs 1 mo · Bengaluru, Karnataka, India · Remote

• Product Name: MLOps Platform – Model Deployment and Automation
• Domain: MLOps, Cloud, DevOps
• Description: The MLOps Platform for Model Deployment and Automation enables scalable ML workflows across cloud environments. As a Python developer, I built backend services using Python, Flask, and SQL, and deployed high-availability ML models Deployment micrservices on Kubernetes with efficient autoscaling. I also worked on CI/CD pipelines to streamline deployments and improve reliability. Additionally, I managed multi-region AWS infrastructure—including SageMaker, EKS, and EC2—using Terraform, implemented monitoring and drift detection to maintain model accuracy, and collaborated with teams to deliver product enhancements while ensuring security through tools like Wiz and Veracode.
• Key Initiatives:
• Spearheaded the development and deployment of automated CI/CD pipelines, significantly reducing deployment times from days to hours while ensuring high model performance.
• Led the deployment of scalable ML models using Kubernetes, ensuring systems can handled increase in traffic without downtime.
• Implemented robust monitoring systems for real-time model performance tracking and drift detection, maintaining prediction accuracy over time.
• Utilized Terraform to provision scalable cloud infrastructure.
• Managed multi-region AWS resources for model deployment, including SageMaker, EKS, and EC2 instances, ensuring high availability and security.
• Collaborated with cross-functional teams to integrate security tools like Wiz and Veracode for ensuring compliance in the deployment process.
• Regularly presented product roadmap and minimum viable product (MVP) versions of new features to stakeholders for feedback and continuous improvement.