We are hiring for leading Data Analytics company, based out in Mumbai & Pune.
Experience Range: 5 - 8 years.
Please note: Immediate or serving Notice period will be preferable.
Location:Pune/Mumbai
Skills: MLOPs, AWS Sagemaker / Azure ML, ELK, Docker
Skills and Qualifications:
- Strong proficiency in Azure ML, DataBricks, ML Flow, and AWS Sagemaker for model development and deployment.
- Proficiency in DevOps practices, CI/CD pipelines, version control systems (e.g., GitHub), and automation using tools like GitHub Actions and YAML.
- Experience with infrastructure as code tools such as Terraform for provisioning and managing cloud resources.
- Expertise in containerization technologies like Docker and orchestration tools like Kubernetes for deploying and managing applications.
- Knowledge of monitoring and observability tools such as Prometheus, Grafana, and ELK stack for tracking performance metrics and logs.
- Familiarity with machine learning concepts and the ability to work closely with data scientists to operationalize models effectively.
- Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment.
Roles & Responsibilities:
- Collaborate with data scientists, engineers, and other stakeholders to streamline the machine learning model deployment process.
- Design, build, and maintain CI/CD pipelines for machine learning model deployment and automation using tools like GitHub Actions, YAML, and Terraform.
- Implement containerization strategies and manage Docker-based deployments for machine learning models.
- Utilize Kubernetes for orchestration and management of containerized applications.
- Develop and maintain monitoring and alerting systems using Prometheus, Grafana, and ELK stack to ensure the health and performance of deployed models.
- Implement and manage machine learning infrastructure on cloud platforms such as Azure ML and AWS Sagemaker.
- Continuously optimize and improve MLOps workflows for scalability, reliability, and efficiency.
Education and Experience:
- Bachelor's or Master's degree in Computer Science, Engineering, or related field.
- Minimum of X years of experience in MLOps, DevOps, or a similar role within the machine learning domain.
- Relevant certifications in Azure, AWS, Kubernetes, or other related technologies are a plus.