Machine Learning Specialist

About the Company : We are seeking a talented and driven Machine Learning Engineer with 2-5 years of experience to join our dynamic team in Chennai. The ideal candidate will have a strong foundation in machine learning principles and extensive hands-on experience in building, deploying, and managing ML models in production environments. A key focus of this role will be on MLOps practices and orchestration, ensuring our ML pipelines are robust, scalable, and automated.

About the Role : A short paragraph summarizing the key role responsibilities.

Responsibilities :

• ML Model Deployment & Management : Design, develop, and implement end-to-end MLOps pipelines for deploying, monitoring, and managing machine learning models in production.
• Orchestration : Utilize orchestration tools (e.g., Apache Airflow, Kubeflow, AWS Step Functions, Azure Data Factory) to automate ML workflows, including data ingestion, feature engineering, model training, validation, and deployment.
• CI / CD for ML : Implement Continuous Integration / Continuous Deployment (CI / CD) practices for ML code, models, and infrastructure, ensuring rapid and reliable releases.
• Monitoring & Alerting : Establish comprehensive monitoring and alerting systems for deployed ML models to track performance, detect data drift, model drift, and ensure operational health.
• Infrastructure as Code (IaC) : Work with IaC tools (e.g., Terraform, CloudFormation) to manage and provision cloud resources required for ML workflows.
• Containerization : Leverage containerization technologies (Docker, Kubernetes) for packaging and deploying ML models and their dependencies.
• Collaboration : Collaborate closely with Data Scientists, Data Engineers, and Software Developers to translate research prototypes into production-ready ML solutions.
• Performance Optimization : Optimize ML model inference and training performance, focusing on efficiency, scalability, and cost-effectiveness.
• Troubleshooting & Debugging : Troubleshoot and debug issues across the entire ML lifecycle, from data pipelines to model serving.
• Documentation : Create and maintain clear technical documentation for MLOps processes, pipelines, and infrastructure.

Qualifications :

Required Skills :