Cloud – MLOps Engineer

Position: Cloud – MLOps Engineer Location: Bangalore (Work from Office) Notice Period: Not more than 30 days Summary We are seeking a highly skilled Cloud – MLOps Engineer to design, implement, and manage scalable cloud-based machine learning infrastructure on AWS . The ideal candidate will have strong expertise in AWS cloud architecture , MLOps pipeline automation , and Large Language Models (LLMs) , along with solid DevOps and CI/CD capabilities. This role involves developing automated, secure, and efficient workflows to support AI-driven applications, enabling continuous training, deployment, and monitoring of models in production environments. Key Skills AWS Cloud, MLOps, Large Language Models (LLMs), Machine Learning Deployment, Model Monitoring, Model Validation, CI/CD Pipelines, DevOps, Infrastructure as Code (IaC), Terraform, CloudFormation, AWS CDK, EC2, S3, Lambda, RDS, IAM, VPC, Jenkins, GitLab CI, AWS CodePipeline, Security by Design, CloudWatch, ELK Stack, Prometheus, Grafana, Automation, Scalability, Performance Optimization, Python, Mentoring, Cloud Architecture Roles and Responsibilities Design, develop, and maintain robust AWS cloud infrastructure to support large-scale, data-intensive applications . Architect and implement scalable solutions using AWS services including EC2, S3, Lambda, RDS, IAM, VPC, and CloudFormation. Develop and manage MLOps pipelines to automate model training, validation, deployment, and monitoring processes. Ensure seamless integration between data pipelines and machine learning workflows for efficient model lifecycle management. Implement and optimize LLM-based solutions , including fine-tuning pre-trained models and deploying AI-driven services in production. Build and maintain CI/CD pipelines using Jenkins, GitLab CI, CircleCI, or AWS CodePipeline to automate testing and deployment. Adopt Infrastructure as Code (IaC) practices with Terraform, AWS CDK, or CloudFormation for consistent, repeatable infrastructure provisioning. Ensure high standards of security, scalability, and performance across all cloud environments. Monitor and analyze cloud environments using AWS CloudWatch, ELK Stack, Prometheus, or Grafana to ensure operational stability. Collaborate with data scientists, developers, and architects to deliver reliable and automated MLOps solutions . Mentor and guide junior engineers , promoting best practices and continuous improvement within the engineering team. Drive technical innovation , exploring new tools, frameworks, and AWS features to enhance AI and cloud capabilities. Requirements Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field. 5+ years of experience in cloud engineering or DevOps, with at least 2+ years in MLOps and model deployment environments. Proven hands-on experience with AWS cloud infrastructure and core services (EC2, S3, RDS, Lambda, VPC, IAM, CloudFormation). Strong proficiency in MLOps frameworks and pipeline orchestration for training, validation, and deployment automation. Experience working with Large Language Models (LLMs) — including fine-tuning, optimization, and production deployment. Solid understanding of CI/CD processes using Jenkins, GitLab CI, or AWS CodePipeline. Practical experience with Infrastructure as Code (IaC) tools such as Terraform, AWS CDK, or CloudFormation. Knowledge of monitoring and observability tools (CloudWatch, ELK, Prometheus, Grafana). Familiarity with security best practices in AWS and DevOps environments. Excellent problem-solving, analytical, and communication skills with a passion for automation and AI innovation. Requirements You will be working with company which based out of Bangalore, showcases the expertise in aerospace and ground transport industries, offering expertise and project management in domains like electrical systems, aerostructures, mechanical, software, systems engineering. It is part of the 3000+ strong engineering services, which is a specialized division of Electrical & Power.