Machine Learning Engineer (ML Ops & Pipelines)

Job Description

As an MLOps Engineer at CurieDx, a Johns Hopkinsaffiliated digital health startup, you will be responsible for owning the data pipelines that power the AI platform and infrastructure. Your role is critical in moving deep learning models from research to real-world clinical deployment in a reliable, secure, and scalable manner.**Key Responsibilities:**- **ML Pipeline Engineering:** - Build and maintain AWS SageMaker pipelines for training, validation, and deployment - Implement experiment tracking and model versioning - Automate retraining workflows- **Data Engineering for ML:** - Write Python scripts for ingesting, cleaning, transforming, and validating metadata datasets - Develop preprocessing and augmentation pipelines for image and other data formats - Structure data to enable ML engineers to immediately begin model development - Maintain dataset versioning and lineage- **Infrastructure & Cloud Architecture:** - Design AWS architecture for GPU training workloads - Manage S3 data storage, IAM roles, networking, and security configurations - Optimize cost and compute efficiency - Establish monitoring and logging systems for production ML services- **Production Deployment:** - Containerize and deploy models for inference - Implement performance monitoring and drift detection - Enhance reliability and observability of deployed ML systems**Qualifications Required:**- 3 years of experience in MLOps, ML engineering, or production ML system deployments- Hands-on experience building data pipelines for image/video preprocessing, augmentation, and annotation workflows- Deep expertise in AWS, including SageMaker, EC2 (GPU instances), S3, Lambda, and broader AWS ecosystem for ML workloads- Experience with CI/CD pipelines, Docker containerization, and orchestration tools like Airflow and Step Functions- Familiarity with annotation tools and data labeling workflows- Comfortable operating in lean environments, being scrappy, resourceful, and action-orientedIf you are passionate about creating a meaningful impact and thrive in a collaborative and innovative environment, CurieDx offers you the opportunity to work with cutting-edge technology backed by renowned institutions like Johns Hopkins, Microsoft, and the National Institutes of Health.CurieDx is an equal opportunity employer that values diversity without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. As an MLOps Engineer at CurieDx, a Johns Hopkinsaffiliated digital health startup, you will be responsible for owning the data pipelines that power the AI platform and infrastructure. Your role is critical in moving deep learning models from research to real-world clinical deployment in a reliable, secure, and scalable manner.**Key Responsibilities:**- **ML Pipeline Engineering:** - Build and maintain AWS SageMaker pipelines for training, validation, and deployment - Implement experiment tracking and model versioning - Automate retraining workflows- **Data Engineering for ML:** - Write Python scripts for ingesting, cleaning, transforming, and validating metadata datasets - Develop preprocessing and augmentation pipelines for image and other data formats - Structure data to enable ML engineers to immediately begin model development - Maintain dataset versioning and lineage- **Infrastructure & Cloud Architecture:** - Design AWS architecture for GPU training workloads - Manage S3 data storage, IAM roles, networking, and security configurations - Optimize cost and compute efficiency - Establish monitoring and logging systems for production ML services- **Production Deployment:** - Containerize and deploy models for inference - Implement performance monitoring and drift detection - Enhance reliability and observability of deployed ML systems**Qualifications Required:**- 3 years of experience in MLOps, ML engineering, or production ML system deployments- Hands-on experience building data pipelines for image/video preprocessing, augmentation, and annotation workflows- Deep expertise in AWS, including SageMaker, EC2 (GPU instances), S3, Lambda, and broader AWS ecosystem for ML workloads- Experience with CI/CD pipelines, Docker containerization, and orchestration tools like Airflow and Step Functions- Familiarity with annotation tools and data labeling workflows- Comfortable operating in lean environments, being scrappy, resourceful, and action-orientedIf you are passionate about creating a meaningful impact and thrive in a collaborative and innovative environment, CurieDx offers you the opportunity to work with cutting-edge technology backed by renowned institutions like Johns Hopkins, Microsoft, and the National Institutes of Health.CurieDx is an equal opportunity employer that values diversity without regard to race, color, religion, gender, gender identity or expre

Keyskills :
PythonDockerAirflowMLOpsML engineeringAWS SageMakerAWS architectureCICD pipelines