Cloud-Based Machine Learning Solution Architect
6 days ago
We are seeking an experienced professional to design and build scalable, secure, and automated ML pipelines. You will play a key role in bridging the gap between data science and engineering teams, driving production readiness of ML models, and ensuring efficient lifecycle management.
About the RoleThis is a unique opportunity to leverage your expertise in AWS services and machine learning deployment best practices. As a solution architect, you will be responsible for designing, building, and maintaining robust MLOps pipelines.
- Design and Implement MLOps Pipelines: Develop and deploy scalable, secure, and automated ML pipelines using Amazon SageMaker Pipelines, CodePipeline, Step Functions, etc.
- Build and Maintain CI/CD Pipelines: Create and manage robust CI/CD pipelines for ML using Amazon SageMaker, S3, Lambda, ECR, EC2, and other relevant tools.
- Automate Model Training and Deployment: Automate model training, evaluation, deployment, and monitoring processes to ensure efficient model lifecycle management.
To be successful in this role, you will need:
- 8+ years of experience in software engineering, DevOps, or ML Engineering with a focus on cloud-based ML pipelines.
- Strong experience with Amazon Web Services (AWS) and its services including Amazon SageMaker, S3, Lambda, Step Functions, CodePipeline, ECR, and CloudWatch.
- Proficiency in Python, Bash, and scripting for automation.
- Familiarity with CI/CD tools like Jenkins, GitHub Actions, CodeBuild, etc.
- Experience with Docker and container orchestration in AWS (ECS, EKS).
- Understanding of ML lifecycle, including feature engineering, training, deployment, and monitoring.
- Experience with data versioning and model tracking tools (MLflow, DVC, SageMaker Model Registry).
- Excellent communication and collaboration skills.
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