Gopal Gautam

MLOPS Engineer

MindMap Digital • Full-time

Nov 2025 - Present • 11 mos

Experienced in designing and managing AI infrastructure and MLOps pipelines to support scalable machine learning and AI-driven applications. Skilled in deploying and managing containerized AI workloads using Kubernetes, Docker, and cloud platforms such as AWS and Google Cloud. Proficient in building automated CI/CD pipelines for machine learning workflows, enabling efficient model deployment, testing, and continuous integration. Experienced in provisioning and managing cloud infrastructure using Terraform, ensuring reliable, scalable, and cost-efficient environments for AI systems. Strong expertise in monitoring and observability using tools like Prometheus and Grafana, ensuring high availability and performance of production systems. Adept at implementing secure infrastructure practices using HashiCorp Vault and AWS IAM for secrets management and access control. Focused on delivering high-performance, scalable, and production-ready AI platforms by integrating DevOps best practices with modern MLOps and cloud-native technologies.

DevOps Engineer

Aesthisia Datacenters • Full-time

Feb 2024 - Nov 2025 • 1 yr 9 mos

Deployed and managed containerized applications on AWS EKS and Google Kubernetes Engine (GKE) using Helm, ensuring high availability, scalability, and reliable production deployments. Built and maintained CI/CD pipelines using GitHub Actions, GitLab CI, Concourse CI, Jenkins, and Argo CD to automate build, testing, and Kubernetes deployment workflows, reducing release cycle time by ~25%. Designed and deployed AI/ML infrastructure to support model training and inference services, enabling scalable AI workloads on cloud and Kubernetes environments. Implemented AI model deployment pipelines integrating containerized machine learning models with Kubernetes, ensuring reliable and scalable inference services. Implemented and managed HashiCorp Vault integrated with AWS IAM for secure secrets management and access control across cloud and AI workloads. Provisioned and optimized multi-cloud infrastructure using Terraform, improving reliability, scalability, and cost efficiency across AWS and GCP environments. Achieved 20–30% cloud cost reduction by migrating workloads to AWS Fargate and optimizing resource allocation strategies. Enhanced application performance and uptime using AWS CloudFront, Auto Scaling, and network optimization strategies. Monitored system health and infrastructure performance using Prometheus, Grafana, and Fluent Bit, proactively maintaining 99.99% uptime.