Data Scientist - MLOps

Job Title : Data Scientist with MLOps Experience : 5-7 Years Location : Bangalore Key Responsibilities : - Develop, train, and deploy machine learning models to address complex business problems. - Lead the implementation of MLOps practices, including automation of model deployment, versioning, and monitoring. - Build and maintain end-to-end machine learning pipelines, integrating them into production environments using AWS, GCP, or Azure platforms. - Collaborate with cross-functional teams (Data Engineers, Software Developers, and Product Managers) to design scalable data pipelines and workflows for model deployment. - Optimize machine learning models for performance, scalability, and cost-efficiency in production. - Automate and streamline the model training, testing, and deployment processes to enable continuous model delivery. - Implement model versioning, monitoring, and retraining pipelines to track performance and improve models over time. - Ensure the reliability and robustness of models, troubleshoot issues, and optimize workflows. - Work with monitoring tools and logs to track the performance of models and detect data drift. - Keep up with the latest trends and tools in machine learning, MLOps, and cloud-based technologies to improve processes and stay ahead of industry Skills & Qualifications : - 5-7 years of experience in Data Science with a focus on machine learning model development, deployment, and MLOps. - Proficiency in Python, R, and machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, and XGBoost. - Hands-on experience with MLOps tools and frameworks, such as MLflow, Kubeflow, Seldon, or TFX. - Experience working with cloud platforms such as AWS, Google Cloud, or Microsoft Azure for building and deploying machine learning solutions. - Expertise in building CI/CD pipelines for machine learning models and automating model workflows. - Strong understanding of containerization (e.g., Docker) and orchestration frameworks (e.g., Kubernetes) for deploying machine learning models at scale. - Experience in model monitoring, data drift detection, and model performance management in production environments. - Strong understanding of data preprocessing, feature engineering, and handling both structured and unstructured data. - Familiarity with SQL and experience working with both relational and NoSQL databases. - Excellent problem-solving, analytical, and communication skills. - Experience with version control systems such as Git. Preferred Skills : - Experience with cloud-native tools such as SageMaker, Azure ML, or GCP AI Platform. - Knowledge of data lakes, data warehouses, and big data technologies such as Apache Spark. - Familiarity with model explainability tools like LIME and SHAP. - Knowledge of DevOps practices, containerization, and distributed computing. (ref:hirist.tech)

Location: bangalore, IN

Posted Date: 5/1/2025