MLOps Architect
Role: MLOps Architect
Location: New York, NY (Remote)
Responsibilities:
• Architect for scalable, cost-efficient, reliable and secure MLOps solution.
• Design, implement and deploy MLOps solutions in AWS.
• Select and justify appropriate ML technology within AWS and Identify appropriate AWS services to implement MLOps 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.
Experience:
• Minimum 10+ years of experience in MLOps with a proven track record of successful deployments.
• In-depth working knowledge of MLOps tools and platforms (Kubernetes, Docker, Jenkins, Git, MLflow, JupyterHub, LLM-specific tooling).
• In-depth working knowledge of AWS and infrastructure as code (IaC) principles.
• Strong Experience with DevOps methodologies and CI/CD pipelines such as Github Actions.
• Strong understanding of machine learning pipelines, model training frameworks, and monitoring techniques.
• Strong programming skills in Python
• Experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit-learn.
• Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
• Experience with large language models (LLMs) and their unique operational considerations is a plus.
• Excellent communication, collaboration, and problem-solving skills.
• The ability to translate technical concepts into clear and concise language.
• A passion for innovation and a drive to optimize ML and LLM workflows
• 12+ years of experience in MLOps, DevOps, or related fields.
• Hands-on experience with AWS.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.
• In depth Knowledge of infrastructure-as-code tools such as AWS CDK and Cloudformation.
• 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 A/B testing and model performance monitoring
Apply Now
Apply Now
Location: New York, NY (Remote)
Responsibilities:
• Architect for scalable, cost-efficient, reliable and secure MLOps solution.
• Design, implement and deploy MLOps solutions in AWS.
• Select and justify appropriate ML technology within AWS and Identify appropriate AWS services to implement MLOps 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.
Experience:
• Minimum 10+ years of experience in MLOps with a proven track record of successful deployments.
• In-depth working knowledge of MLOps tools and platforms (Kubernetes, Docker, Jenkins, Git, MLflow, JupyterHub, LLM-specific tooling).
• In-depth working knowledge of AWS and infrastructure as code (IaC) principles.
• Strong Experience with DevOps methodologies and CI/CD pipelines such as Github Actions.
• Strong understanding of machine learning pipelines, model training frameworks, and monitoring techniques.
• Strong programming skills in Python
• Experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit-learn.
• Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
• Experience with large language models (LLMs) and their unique operational considerations is a plus.
• Excellent communication, collaboration, and problem-solving skills.
• The ability to translate technical concepts into clear and concise language.
• A passion for innovation and a drive to optimize ML and LLM workflows
• 12+ years of experience in MLOps, DevOps, or related fields.
• Hands-on experience with AWS.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.
• In depth Knowledge of infrastructure-as-code tools such as AWS CDK and Cloudformation.
• 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 A/B testing and model performance monitoring
Apply Now
Apply Now