Dipanshu Maddheshiya

About Dipanshu Maddheshiya

As a DevOps & MLOps Engineer, I specialize in designing and implementing scalable automation frameworks that accelerate product delivery, enhance infrastructure reliability, and streamline ML model deployment. With expertise in AWS, Azure, GCP, Kubernetes, Docker, Terraform, and CI/CD tools like Jenkins and GitLab, I have built end-to-end solutions that ensure high availability, faster releases, and seamless ML operations.At Carelon Global Solutions, I engineered cloud-native CI/CD pipelines that automated application deployments and ML workflows—reducing manual effort by 70% and improving release frequency by 3x. By integrating monitoring tools such as Prometheus, Grafana, and ELK, I enabled proactive system health tracking and root-cause analysis, ensuring zero unplanned downtime in critical environments.I have also worked on MLOps initiatives that streamlined the ML lifecycle using MLflow, Kubeflow, and Airflow—covering model versioning, deployment, monitoring, and automated retraining. These pipelines bridged data science and production environments, cutting model deployment time from weeks to hours and ensuring reliable, scalable AI solutions.My passion lies in leveraging automation, cloud, and AI-driven operations to deliver secure, resilient, and efficient systems. I thrive in solving complex infrastructure challenges, optimizing workflows, and enabling teams to innovate without bottlenecks.Let’s connect if you’re interested in cloud automation, DevOps best practices, or scaling ML workflows with MLOps.

Experience

1. Software Engineer i

   Carelon

   Sep 2023 — Present · Gurugram, IN

   Designed and implemented secure, scalable AWS cloud infrastructures leveraging EC2, EKS, S3, Lambda, RDS, and IAM, ensuring compliance with U.S. Federal healthcare regulations (CAA-2021).~ Streamlined infrastructure provisioning and configuration management with Terraform, Ansible, and CloudFormation, boosting deployment speed and reliability.~ Optimized CI/CD pipelines using Jenkins, GitLab CI/CD, and GitHub Actions, reducing release cycle time by 40% and accelerating delivery across teams.~ Delivered and monitored containerized applications with Docker, Kubernetes, and Helm, achieving high availability, scalability, and fault tolerance.~ Integrated MLOps practices by deploying ML models with SageMaker and MLflow, and orchestrated retraining pipelines to maintain model performance and accuracy.~ Enhanced observability by implementing Prometheus, Grafana, ELK Stack, and CloudWatch, reducing MTTR (Mean Time to Resolution) by 25%.~ Built data ingestion and transformation workflows using Glue, PySpark, and Airflow, enabling real-time analytics and faster decision-making.~ Collaborated with global teams across the U.S. and Europe to deliver cloud-native, secure, and cost-optimized solutions for healthcare and insurance clients.~ Demonstrated strong ownership in high-pressure environments by leading critical production releases with zero downtime.Tech Stack & Skills:AWS (EC2, S3, Lambda, EKS, RDS, Glue, Redshift, SageMaker, Forecast, IAM, CloudFormation) · Jenkins · GitLab CI/CD · GitHub Actions · Docker · Kubernetes · Helm · Terraform · Ansible · MLflow · PySpark · Airflow · Prometheus · Grafana · ELK Stack · CloudWatch · Datadog · SQL · Python · Bash · Linux Administration

Education

• DAV Narang Intermediate College

  12th, Mathematics

• DAV Narang Intermediate College

  10th, Mathematics

• Lovely Professional University

  Bachelor of Technology - BTech, Computer Science and Engineering

  2018 — 2022