Project Role : Custom Software Engineer
Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Must have skills : Machine Learning Operations
Good to have skills : Python (Programming Language), AWS Architecture, AWS AI Services
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary We are seeking an experienced MLOps Engineer with strong expertise in building, automating, and maintaining end-to-end Machine Learning pipelines on AWS. The ideal candidate will have hands-on experience with AWS ML services, Infrastructure-as-Code, CI/CD for ML workflows, and scalable model deployment. This role requires strong collaboration, operational excellence, and the ability to support production-grade ML systems. ________________________________________ Roles & Responsibilities • Design and implement end-to-end ML pipelines—including data processing, model training, validation, deployment, and monitoring—using AWS services such as SageMaker, Lambda, Step Functions, ECS/ECR, and S3. • Develop Infrastructure-as-Code (IaC) using AWS CloudFormation, Terraform, or AWS CDK to automate deployments and resource provisioning. • Monitor and maintain model performance in production environments; build CI/CD workflows for ML models using CodePipeline, CodeBuild, and CodeDeploy. • Collaborate with data scientists, engineering teams, and DevOps to streamline model lifecycle management and ensure scalable ML operations. • Automate model retraining, rollback, and validation processes; establish robust testing and quality checks. • Manage containerization and orchestration using Docker, Kubernetes, EKS/ECS. • Document ML pipelines, operational procedures, and best practices to ensure long-term system maintainability. ________________________________________ Professional & Technical Skills • Hands-on experience with AWS AI/ML and compute services (SageMaker, Lambda, S3, IAM, Step Functions, CloudFormation, ECS/EKS). • Strong background in MLOps, including ML model lifecycle management, automation, monitoring, and production support. • Proficiency in CI/CD workflows and DevOps tooling (Git, CodePipeline, CodeBuild, CloudWatch, Prometheus). • Knowledge of security, compliance, and cloud governance best practices (e.g., HIPAA, GDPR). • Strong communication, documentation, and cross-functional collaboration skills. ________________________________________ Additional Information • Experience with large-scale distributed systems is a plus. • Familiarity with feature stores, model registries, or domain-specific ML frameworks is an advantage. • Ability to work in fast-paced environments and support multiple ML initiatives concurrently. • Certifications in AWS (e.g., AWS Certified Machine Learning – Specialty, DevOps Engineer – Professional) are preferred but not mandatory.

15 years full time education

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