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PDS Inc, LLC
http://www.pdsinc.com
http://www.pdsinc.com
true
ML Ops Engineer
Posted: 11/14/2024
2024-11-14
2025-01-10
Employment Type:
Contract
Job Category: AI/ML
Job Number: 24134
Workplace Type: Remote
Job Description
ML Ops Engineer
Compensation: DOE
?
POSITION SUMMARY: An MLOps 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 QUALIFICATIONS:
• Bachelor’s degree in IT or related field.
• 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.
We look forward to reviewing your application. We encourage everyone to apply - even if every box isn’t checked for what you are looking for or what is required.
PDSINC, LLC is an Equal Opportunity Employer.
Compensation: DOE
?
POSITION SUMMARY: An MLOps 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 QUALIFICATIONS:
• Bachelor’s degree in IT or related field.
• 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.
We look forward to reviewing your application. We encourage everyone to apply - even if every box isn’t checked for what you are looking for or what is required.
PDSINC, LLC is an Equal Opportunity Employer.
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