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🚀 Hiring: MLOps Engineer (AWS) |
Contract Role
📍 Location Preference: Austin, TX (Highly Preferred) | CST (Second Preference) | Remote (US – Last Preference)
🔍 About the Role
We are seeking a highly experienced MLOps Engineer to design, build, and manage scalable machine learning infrastructure on AWS. This role focuses on end-to-end ML lifecycle management—from automated training pipelines and experiment tracking to deployment, monitoring, and continuous retraining.
You will play a key role in bridging Data Science and Engineering, ensuring reliable and efficient delivery of ML solutions at scale using AWS-native services and tools like SageMaker, Kubeflow, and MLflow.
🛠️ Key Responsibilities
Design and manage scalable AWS-based MLOps infrastructure
Build end-to-end ML pipelines using SageMaker Pipelines, Step Functions, Kubeflow
Implement model versioning, experiment tracking, and model registry
Develop and maintain CI/CD pipelines for ML workflows
Deploy models using SageMaker endpoints (real-time & batch)
Enable model monitoring, drift detection, and automated retraining
Implement A/B testing and canary deployments
Work closely with Data Scientists and Engineering teams
Monitor systems using CloudWatch, X-Ray, CloudTrail
✅ Required Skills
Strong experience in Python and ML frameworks (TensorFlow / PyTorch)
Hands-on with AWS SageMaker & SageMaker Pipelines
Expertise in MLflow, Kubeflow
Experience with Docker, Kubernetes (Amazon EKS)
Strong knowledge of CI/CD (CodePipeline, CodeBuild, CodeDeploy)
Proficiency in AWS services (Lambda, S3, Step Functions, Bedrock)
Experience with Infrastructure as Code (CloudFormation / CDK)
Strong understanding of Model Monitoring, Drift Detection, Model Registry
🧠 Skills Evaluated
Python | AWS SageMaker | SageMaker Pipelines | MLflow | Kubeflow | Docker | Kubernetes | Amazon EKS | CI/CD | CodePipeline | CodeBuild | MLOps | Model Registry | Model Monitoring | Drift Detection | Step Functions | CloudFormation | Infrastructure-as-Code
🚀 Hiring: MLOps Engineer (AWS) |
Contract Role
📍 Location Preference: Austin, TX (Highly Preferred) | CST (Second Preference) | Remote (US – Last Preference)
🔍 About the Role
We are seeking a highly experienced MLOps Engineer to design, build, and manage scalable machine learning infrastructure on AWS. This role focuses on end-to-end ML lifecycle management—from automated training pipelines and experiment tracking to deployment, monitoring, and continuous retraining.
You will play a key role in bridging Data Science and Engineering, ensuring reliable and efficient delivery of ML solutions at scale using AWS-native services and tools like SageMaker, Kubeflow, and MLflow.
🛠️ Key Responsibilities
Design and manage scalable AWS-based MLOps infrastructure
Build end-to-end ML pipelines using SageMaker Pipelines, Step Functions, Kubeflow
Implement model versioning, experiment tracking, and model registry
Develop and maintain CI/CD pipelines for ML workflows
Deploy models using SageMaker endpoints (real-time & batch)
Enable model monitoring, drift detection, and automated retraining
Implement A/B testing and canary deployments
Work closely with Data Scientists and Engineering teams
Monitor systems using CloudWatch, X-Ray, CloudTrail
✅ Required Skills
Strong experience in Python and ML frameworks (TensorFlow / PyTorch)
Hands-on with AWS SageMaker & SageMaker Pipelines
Expertise in MLflow, Kubeflow
Experience with Docker, Kubernetes (Amazon EKS)
Strong knowledge of CI/CD (CodePipeline, CodeBuild, CodeDeploy)
Proficiency in AWS services (Lambda, S3, Step Functions, Bedrock)
Experience with Infrastructure as Code (CloudFormation / CDK)
Strong understanding of Model Monitoring, Drift Detection, Model Registry
🧠 Skills Evaluated
Python | AWS SageMaker | SageMaker Pipelines | MLflow | Kubeflow | Docker | Kubernetes | Amazon EKS | CI/CD | CodePipeline | CodeBuild | MLOps | Model Registry | Model Monitoring | Drift Detection | Step Functions | CloudFormation | Infrastructure-as-Code