What makes you a great fit:

• Operationalize Machine Learning Models: Deploy, monitor, and manage ML models in production using industry standard stack, ensuring scalability, reliability, and performance.
• Collaborate with Data Science Teams: Work closely with data scientists to transition models from development to production, providing expertise in deployment strategies and best practices.
•  Optimize ML Pipelines: Design and optimize ML pipelines for efficient model training, testing, and deployment. 
• Implement CI/CD processes to automate and streamline the ML lifecycle.
• Manage Cloud Environments: Oversee the configuration, scaling, and management of Cloud
• environments to support ML operations, integrating with upstream and downstream systems as required.
• Implement Monitoring Solutions: Develop and maintain monitoring tools and dashboards to track model performance, detect anomalies, and ensure model accuracy over time.
• Provide Technical Support: Offer guidance and troubleshooting for ML models in production, addressing any issues related to performance, data quality, or system integration.
• Undergraduate Degree in a quantitative discipline such as Engineering, Computer Science, Mathematics, or a related field from a top-tier institution.
• Minimum 3 years of experience in ML Ops, Data Engineering, or a related field, with a focus on deploying and managing ML models in production.
• Hands-on experience with leading ML and Data tools such as MLflow, Spark, Pytorch etc.
• Strong understanding of cloud platforms such as AWS, Azure, or GCP, with experience in managing scalable ML environments.
• Familiarity with CI/CD pipelines, containerization (e.g., Docker), and orchestration tools (e.g.,Airflow, Jenkins).