Compunnel
Job Summary
We are seeking a Machine Learning Operations (MLOps) Engineer to support the development and maintenance of infrastructure and tools for deploying and monitoring machine learning models.
This role is part of a team that delivers modern cloud-based data solutions to support Sales, Marketing, Finance, and AI/ML analytics capabilities.
The ideal candidate will collaborate closely with data scientists and engineers to ensure scalable, secure, and efficient model operations.
Key Responsibilities Model Deployment and Management: Develop and manage scalable and reliable deployment processes for machine learning models in production environments. Infrastructure Automation: Design and implement automated workflows for model training, testing, and deployment using tools such as Jenkins, Docker, and Kubernetes. Aerospike Management: Utilize Aerospike for high-performance data storage and retrieval to support ML applications. API Development: Build and maintain robust APIs for integrating models with various applications and services. Collaboration: Work closely with cross-functional teams to translate business requirements into technical solutions. Monitoring and Optimization: Implement monitoring tools to track model performance and system health, and optimize infrastructure as needed. Security and Compliance: Ensure all deployed models and systems adhere to industry standards and security protocols. Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 3+ years of experience in MLOps, DevOps, or a related field with a focus on machine learning. 1+ years of experience developing solutions on AWS cloud platform. Proficiency in Python and/or Java with strong coding and debugging skills. Experience with CI/CD practices and tools. Proven experience with container-based systems such as Docker. Preferred Qualifications
Experience with Kubernetes and cloud-native architecture. Familiarity with Aerospike or similar high-performance data stores. Exposure to agile development environments and cross-functional collaboration.
Education:
Bachelors Degree
We are seeking a Machine Learning Operations (MLOps) Engineer to support the development and maintenance of infrastructure and tools for deploying and monitoring machine learning models.
This role is part of a team that delivers modern cloud-based data solutions to support Sales, Marketing, Finance, and AI/ML analytics capabilities.
The ideal candidate will collaborate closely with data scientists and engineers to ensure scalable, secure, and efficient model operations.
Key Responsibilities Model Deployment and Management: Develop and manage scalable and reliable deployment processes for machine learning models in production environments. Infrastructure Automation: Design and implement automated workflows for model training, testing, and deployment using tools such as Jenkins, Docker, and Kubernetes. Aerospike Management: Utilize Aerospike for high-performance data storage and retrieval to support ML applications. API Development: Build and maintain robust APIs for integrating models with various applications and services. Collaboration: Work closely with cross-functional teams to translate business requirements into technical solutions. Monitoring and Optimization: Implement monitoring tools to track model performance and system health, and optimize infrastructure as needed. Security and Compliance: Ensure all deployed models and systems adhere to industry standards and security protocols. Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 3+ years of experience in MLOps, DevOps, or a related field with a focus on machine learning. 1+ years of experience developing solutions on AWS cloud platform. Proficiency in Python and/or Java with strong coding and debugging skills. Experience with CI/CD practices and tools. Proven experience with container-based systems such as Docker. Preferred Qualifications
Experience with Kubernetes and cloud-native architecture. Familiarity with Aerospike or similar high-performance data stores. Exposure to agile development environments and cross-functional collaboration.
Education:
Bachelors Degree