Role Description

We are looking for a Machine Learning Engineer / MLOps Engineer with robust expertise in Python, machine learning model automation, and MLOps. The ideal candidate will have hands-on experience building and maintaining automated machine learning pipelines, managing model artifacts and versions, and deploying ML workflows on cloud platforms such as GCP or Azure ML. The role involves developing scalable and reliable ML infrastructure that supports the complete machine learning lifecycle—from data preparation and model training to validation, versioning, deployment, monitoring, and automated retraining. The successful candidate will work closely with Data Scientists, ML Engineers, Data Engineers, Software Engineers, and Product teams to productionize machine learning models and establish robust MLOps practices. Key Responsibilities 1. Python Development Develop production-quality machine learning and MLOps solutions using Python. Build reusable Python libraries, utilities, and automation frameworks for ML workflows. Develop scripts and services for model training,



validation, deployment, monitoring, and retraining. Write clean, maintainable, testable, and well-documented Python code. Implement appropriate logging, exception handling, configuration management, and testing. Optimize Python-based ML workflows for scalability and reliability. 2. Machine Learning Modeling Automation Automate the end-to-end machine learning lifecycle. Build automated workflows for: Data preparation and validation Feature engineering Model training Hyperparameter tuning Model evaluation and validation Model packaging Model registration Deployment Monitoring Model retraining Develop reusable automation components that can support multiple ML models and use cases. Implement automated model validation and quality checks before deployment. Establish standardized processes for moving models from experimentation to production. Reduce manual intervention in model development and dep

📌 Lead I (Bengaluru)
🏢 UST
📍 Bengaluru