Location: Chicago, IL (100% remote)

Duration: 1+ Year

Job Description:

Mandatory skills: KUBEFLOW IS A MANDATORY SKILL

• Architect for scalable, cost-efficient, reliable and secure ML solution.

• Design, implement and deploy ML solutions in AWS.

• Select and justify appropriate ML technology within AWS and Identify appropriate AWS services to implement ML solutions.

• Design, build, and maintain infrastructure required for efficient development, deployment, and monitoring of machine learning models.

• Implement CI/CD pipelines for machine learning applications to ensure smooth development and deployment processes.

• Collaborate with data scientists to understand and implement requirements for model serving, versioning, and reproducibility.

• Monitor and optimize model performance in production, identifying and resolving issues proactively to ensure optimal results.

• Automate repetitive tasks to improve efficiency and reduce the risk of human error in MLOps workflows.

• Maintain documentation and provide training to team members on MLOps best practices, ensuring knowledge sharing and collaboration within the team.

• Stay updated with the latest developments in MLOps tools, technologies, and methodologies to remain current and effective in your role.

Skills: –

• 3+ years of experience in MLOps, Kubeflow, DevOps, or related fields.

• Strong programming skills in Python, GoLang with experience in other languages such as Java, C++, or Scala being a plus.

• Experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit-learn.

• Proficiency with CI/CD tools such as Github Actions.

• Hands-on experience with AWS.

• Familiarity with containerization and orchestration tools like Docker and Kubernetes.

• Knowledge of infrastructure-as-code tools such as AWS CDK and Cloudformation.

• Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.

• Excellent problem-solving skills and the ability to work independently as well as part of a team.

• Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.

Preferred Qualifications: –

• AWS Certified Machine Learning – Specialty

• Experience with feature stores, model registries, and monitoring tools such as MLflow, Tecton, or Seldon.

• Familiarity with data engineering tools such as AWS EMR, Glue and Apache Spark.

• Knowledge of security best practices for machine learning systems.

• Experience with A/B testing and model performance monitoring

Educational Qualifications: –

Bachelor’s or master’s degree in computer science, Engineering, or a related field

•Certification: AWS Certified Machine Learning

From:
Praveen Kumar,
Magicforce
praveen@magicforce.us
Reply to: praveen@magicforce.us