AWS Cloud Data Engineer
3 weeks ago
About Us
We are a pioneering biotech company, leveraging AI and machine learning to drive innovation in healthcare and life sciences. Our focus is on harnessing large-scale data to drive research and product development, improving patient outcomes. We seek a Cloud Data Engineer with a strong ML Ops focus to build and maintain our data-driven infrastructure.
Role Overview
As a Cloud Data Engineer specializing in ML Ops, you will play a critical role in the machine learning lifecycle. Your focus will be on developing, automating, and scaling our cloud infrastructure to support model deployment, monitoring, and optimization. You will work closely with data scientists and engineers to streamline workflows, ensuring efficient model deployment, frequent retraining, and effective monitoring.
Key Responsibilities
- Design, build, and manage scalable data and ML infrastructure on AWS, leveraging services like S3, Lambda, EC2, and EKS.
- Implement robust ML Ops pipelines using AWS SageMaker, Docker, and Kubernetes to automate model training, testing, deployment, and monitoring.
- Collaborate with data scientists to optimize model serving, including A/B testing, continuous retraining, and performance tuning.
- Build CI/CD pipelines to ensure fast and reliable deployment of ML models and data processing workflows.
- Monitor, troubleshoot, and optimize machine learning pipelines and cloud resources to ensure high availability and performance.
- Ensure security, compliance, and best practices in cloud architecture and data handling.
Key Requirements
- Proven experience in AWS Cloud services, especially for machine learning and data engineering tasks.
- Proficiency with AWS SageMaker and related ML Ops tools and technologies.
- Experience with containerization (Docker, Kubernetes) and orchestrating machine learning workflows.
- Strong programming skills in Python, with experience in building data pipelines.
- Solid understanding of DevOps and ML Ops principles, including monitoring, version control, and automation.
- Knowledge of big data frameworks like Spark and experience working with large-scale datasets is a plus.
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