DevOps Engineer at Version1

About the Role

We are looking for an experienced AWS MLOps / DevOps Engineer to design, automate, and optimize machine learning and data workflows on AWS. You will play a central role in building scalable, secure, and reliable ML infrastructure that accelerates model development and deployment across the organization.

Key Responsibilities

• →MLOps & Machine Learning Workflow Automation
• →Build and maintain end-to-end ML pipelines using Amazon SageMaker, Step Functions, Lambda, and Glue.
• →Implement model lifecycle workflows: data preprocessing, feature engineering, model training, model registry, deployment, and monitoring.
• →Automate model deployment to real-time endpoints and batch systems.
• →Establish model monitoring, including data drift, model drift, performance metrics, and automated retraining triggers.AWS Cloud Engineering
• →Design, automate, and maintain AWS infrastructure using AWS CloudFormation and/or Terraform.
• →Build scalable data and ML environments using AWS services such as S3, ECR, ECS/EKS, Lambda, VPC, IAM, and CloudWatch.
• →Build Spark-based ETL pipelines using AWS Glue, EMR, or Spark on Kubernetes.
• →Ensure compliance with AWS security best practices including IAM governance and encryption.DevOps, CI/CD & Automation
• →Develop CI/CD pipelines for ML and data workflows using GitHub Actions, GitLab CI, Jenkins, or CodePipeline.
• →Implement automated testing for data validation, model quality, pipeline integrity, and infrastructure deployments.
• →Maintain logging, monitoring, and observability systems using CloudWatch, Prometheus/Grafana, or ELK.Collaboration & Technical Leadership
• →Work closely with Data Scientists to productionize notebooks, scripts, and models.
• →Collaborate with Data Engineering to align data processing workflows with ML requirements.
• →Drive best practices in automation, model deployment, and cloud architecture.