MLOps Engineer - Remote (AWS Certified Machine Learning)
Position : MLOps Engineer - Remote (AWS Certified Machine Learning)
Location : San Diego, CA
Duration : 10+ Months
Total Hours/week : 40
1st Shift
Client : Medical Devices Company
Level of Experience : Senior Level
Employment Type : Contract on W2 (Need US Citizens or GC Holders or GC EAD or OPT or EAD or CPT)
Job Description
ā¢ We're seeking an experienced MLOps Engineer to lead the operationalization of our Machine Learning workloads.
ā¢ As a key team member, you'll be responsible for designing, building, and maintaining infrastructure required for efficient development, deployment, and monitoring of machine learning workloads.
ā¢ Your close collaboration with data scientists will ensure that our models are reliable, scalable, and performing optimally.
ā¢ This role requires expertise in automating ML workflows, enhancing model reproducibility, and ensuring continuous integration and delivery.
Responsibilities
ā¢ Architect for scalable, cost-efficient, reliable and secure ML solution.
ā¢ Design, implement and deploy ML solutions in AWS.
ā¢ Select and justify appropriate ML technology within AWS and Identify appropriate AWS services to implement ML solutions.
ā¢ Design, build, and maintain infrastructure required for efficient development, deployment, and monitoring of machine learning models.
ā¢ Implement CI/CD pipelines for machine learning applications to ensure smooth development and deployment processes.
ā¢ Collaborate with data scientists to understand and implement requirements for model serving, versioning, and reproducibility.
ā¢ Monitor and optimize model performance in production, identifying and resolving issues proactively to ensure optimal results.
ā¢ Automate repetitive tasks to improve efficiency and reduce the risk of human error in MLOps workflows.
ā¢ Maintain documentation and provide training to team members on MLOps best practices, ensuring knowledge sharing and collaboration within the team.
ā¢ Stay updated with the latest developments in MLOps tools, technologies, and methodologies to remain current and effective in your role.
Qualifications
ā¢ Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
ā¢ 3+ years of experience in MLOps, DevOps, or related fields.
ā¢ Strong programming skills in Python, GoLang with experience in other languages such as Java, C++, or Scala being a plus.
ā¢ Experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit-learn.
ā¢ Proficiency with CI/CD tools such as Github Actions.
ā¢ Hands-on experience with AWS.
ā¢ Familiarity with containerization and orchestration tools like Docker and Kubernetes.
ā¢ Knowledge of infrastructure-as-code tools such as AWS CDK and Cloudformation.
ā¢ Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
ā¢ Excellent problem-solving skills and the ability to work independently as well as part of a team.
ā¢ Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.
Preferred Qualifications
ā¢ AWS Certified Machine Learning - Specialty
ā¢ Experience with feature stores, model registries, and monitoring tools such as MLflow, Tecton, or Seldon.
ā¢ Familiarity with data engineering tools such as AWS EMR, Glue and Apache Spark.
ā¢ Knowledge of security best practices for machine learning systems.
ā¢ Experience with A/B testing and model performance monitoring.
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Location : San Diego, CA
Duration : 10+ Months
Total Hours/week : 40
1st Shift
Client : Medical Devices Company
Level of Experience : Senior Level
Employment Type : Contract on W2 (Need US Citizens or GC Holders or GC EAD or OPT or EAD or CPT)
Job Description
ā¢ We're seeking an experienced MLOps Engineer to lead the operationalization of our Machine Learning workloads.
ā¢ As a key team member, you'll be responsible for designing, building, and maintaining infrastructure required for efficient development, deployment, and monitoring of machine learning workloads.
ā¢ Your close collaboration with data scientists will ensure that our models are reliable, scalable, and performing optimally.
ā¢ This role requires expertise in automating ML workflows, enhancing model reproducibility, and ensuring continuous integration and delivery.
Responsibilities
ā¢ Architect for scalable, cost-efficient, reliable and secure ML solution.
ā¢ Design, implement and deploy ML solutions in AWS.
ā¢ Select and justify appropriate ML technology within AWS and Identify appropriate AWS services to implement ML solutions.
ā¢ Design, build, and maintain infrastructure required for efficient development, deployment, and monitoring of machine learning models.
ā¢ Implement CI/CD pipelines for machine learning applications to ensure smooth development and deployment processes.
ā¢ Collaborate with data scientists to understand and implement requirements for model serving, versioning, and reproducibility.
ā¢ Monitor and optimize model performance in production, identifying and resolving issues proactively to ensure optimal results.
ā¢ Automate repetitive tasks to improve efficiency and reduce the risk of human error in MLOps workflows.
ā¢ Maintain documentation and provide training to team members on MLOps best practices, ensuring knowledge sharing and collaboration within the team.
ā¢ Stay updated with the latest developments in MLOps tools, technologies, and methodologies to remain current and effective in your role.
Qualifications
ā¢ Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
ā¢ 3+ years of experience in MLOps, DevOps, or related fields.
ā¢ Strong programming skills in Python, GoLang with experience in other languages such as Java, C++, or Scala being a plus.
ā¢ Experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit-learn.
ā¢ Proficiency with CI/CD tools such as Github Actions.
ā¢ Hands-on experience with AWS.
ā¢ Familiarity with containerization and orchestration tools like Docker and Kubernetes.
ā¢ Knowledge of infrastructure-as-code tools such as AWS CDK and Cloudformation.
ā¢ Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
ā¢ Excellent problem-solving skills and the ability to work independently as well as part of a team.
ā¢ Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.
Preferred Qualifications
ā¢ AWS Certified Machine Learning - Specialty
ā¢ Experience with feature stores, model registries, and monitoring tools such as MLflow, Tecton, or Seldon.
ā¢ Familiarity with data engineering tools such as AWS EMR, Glue and Apache Spark.
ā¢ Knowledge of security best practices for machine learning systems.
ā¢ Experience with A/B testing and model performance monitoring.
Apply tot his job
Apply To this Job