MLOps Engineer

Job Description:

• We are looking for an experienced MLOps Engineer to deploy, manage, and continuously optimize machine learning workloads running in production environments
• This position is primarily centered on MLOps (60%), with AWS Cloud (25%) and DevOps (15%) responsibilities
• The successful candidate will take hands-on ownership of the full end-to-end ML lifecycle, including model training, deployment, monitoring, automation, performance optimization, and retraining
• Proficiency with AWS services, CI CD pipelines, containerization, infrastructure automation, and production-grade ML platforms is essential
• This is not a general DevOps role; candidates must show proven experience operationalizing and maintaining ML models at scale in cloud environments

Key Responsibilities:

• 5 yrs DevOps Cloud MLOps experience Python for scripting and automation
• Strong working knowledge of Jenkins, Git, Docker, and EKS troubleshooting
• AWS experience including SageMaker, Lambda, S3, ECS, IAM, and RDS, along with infra creation
• Infrastructure as Code with CloudFormation CFT and Terraform
• Build and operate MLOps workflows, including running ML pipelines and deploying to SageMaker, Databricks, or Lambda

Technical Requirements:

• SageMaker
• MLflow
• Kubeflow
• Databricks
• MLOps
• Model Deployment
• Model Monitoring
• Model Retraining
• Feature Store
• CI CD for ML
• Training Pipelines
• Inference Pipelines
• Drift Detection
• Docker
• Kubernetes EKS
• Terraform
• CloudFormation
• AWS Lambda
• Python Automation

Additional Responsibilities:

Preferred Skills:

Technology->AI-AI Engineering->MLOps,Technology->AI-Machine Learning->Databricks Machine Learning