ML Ops Engineer
Description For ML Ops Engineer
Solvd Inc., a premier software engineering company with over 12 years of experience and 800+ international employees, is seeking a seasoned ML Ops Engineer to join their dynamic team. This remote position offers the opportunity to work with cutting-edge technologies and manage machine learning infrastructure for a company that serves digital-native enterprise clients, including some of the biggest brands in retail and social media.
As an ML Ops Engineer, you'll be at the forefront of managing and optimizing end-to-end ML pipelines using advanced tools such as AWS, AWS CloudFormation, AWS SageMaker, and the Databricks Platform, including Unity Catalog. Your responsibilities will span from architecting scalable ML infrastructure to automating model deployment and ensuring continuous availability and performance.
Key aspects of the role include:
- Managing ML infrastructure using AWS services
- Automating model deployment with AWS SageMaker and Databricks
- Utilizing AWS CloudFormation for infrastructure as code
- Leveraging Databricks Unity Catalog for data governance
- Implementing monitoring solutions and optimizing model performance
- Collaborating with data scientists and engineers
- Building and maintaining CI/CD pipelines for ML models
The ideal candidate will have strong expertise in AWS services, CloudFormation, Databricks, and ML deployment. You should be proficient in Python and familiar with ML libraries. A DevOps mindset and experience with containerization tools like Docker and Kubernetes are also valuable.
This role offers the chance to work on challenging projects, contribute to cutting-edge ML solutions, and grow your skills in a rapidly evolving field. If you're passionate about MLOps and want to make a significant impact in a global company, this position at Solvd Inc. could be your next career move.
Responsibilities For ML Ops Engineer
- Architect, manage, and maintain scalable ML infrastructure using AWS services
- Automate the deployment of machine learning models to production
- Use AWS CloudFormation to define and provision infrastructure for ML workloads
- Leverage Databricks Unity Catalog for data governance, security, and compliance
- Implement and monitor models in production
- Work closely with data scientists and data engineers
- Build and maintain CI/CD pipelines for ML models
Requirements For ML Ops Engineer
- Strong knowledge of AWS services, including AWS SageMaker, CloudFormation, EC2, S3, and CloudWatch
- Experience with creating, managing, and automating AWS resources using AWS CloudFormation templates
- Expertise in the Databricks platform and Unity Catalog
- Proven experience in deploying machine learning models to production environments
- Solid understanding of CI/CD pipelines and version control
- Strong coding skills in Python, with knowledge of ML libraries
- Experience setting up and managing monitoring and logging for production models
- Familiarity with DevOps principles and infrastructure automation
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