Remote Role- Machine Learning Ops engineer-

An Machine Learning Ops engineer?is responsible for bridging the gap between machine learning development and operations.

?||?They must have a strong understanding of machine learning concepts and algorithms, as well as proficiency in programming languages such as Python.

||?Experience with the AWS cloud platforms along with knowledge of version control systems, CI/CD pipelines, and infrastructure-as-code practices.

||?They need to be familiar with data processing and storage technologies, including both SQL and NoSQL databases.

||?Understanding of monitoring and logging tools is crucial for maintaining and optimizing ML models in production.

|| Additionally, MLOps engineers should have experience with model experimentation platforms, model deployment, scaling, and A/B testing.

|| Strong problem-solving skills and the ability to collaborate ?effectively with data scientists, software engineers, and other stakeholders are vital for success in this role.

PRINCIPAL RESPONSIBILITIES:

• Leads the development and implementation of ML pipelines, including data preprocessing, model training, and deployment workflows.
• Designs and maintains scalable infrastructure for ML model experimentation and testing.
• Develops and optimizes automated CI/CD pipelines for the applications, services, models, and prompts.
• Implements robust monitoring and logging systems in production, ensuring performance, accuracy, and data drift detection.
• Collaborates with data scientists to streamline the process of moving ML models from experimentation to production environments.
• Designs and implements strategies for model versioning, A/B testing, and deployments of ML models and prompts.
• Develops solutions for efficient data management, including data versioning, feature stores, and data lineage tracking.
• Works cross-functionally with data science, software engineering, and operations teams to integrate ML workflows into existing systems and processes.
• Leverages cloud technologies and MLOps tools to enhance the scalability and efficiency of ML operations.
• Implements best practices for ML model governance, including model explainability and fairness considerations.
• Performs other job-related duties as assigned or apparent.

QUALIFICATIONS:

Experience with agile scrum software development methodologies.

Experience in all aspects of the software development lifecycle: design, functional and technical requirements, coding, debugging, testing, release, and operational support.

Lead team using the following technologies such as:

Should have significant experience in tools like: Terraform or CloudFormation and Amazon Web Services (Lambda, ApiGateway, IAM, Sagemaker, Bedrock, DynamoDB), MLFlow, Docker, Serverless framework, GitHub, GitHub Actions, Terraform Cloud, bash, Linux, YAML, Python or Node.js

Minimum of 5 years of experience in a MLOps Engineer role and Amazon Web Services including previous experience in creating environments using infrastructure as code for mission critical applications

Thanks & Regards?

Satyendra Pandey

1 281 957 4343

[email protected]

Sapot Systems?