Job
Description
We are considering applicants for the location(s) of Hyderabard, India. We are looking for an experienced Machine Learning Engineer with a focus on MLOps to join our dynamic team and ensure the seamless productionization, maintenance, and monitoring of machine learning and AI applications. You will support a range of applications, from traditional classification, forecasting, and prediction models to recommendation systems and LLM-powered solutions. You will collaborate with Machine Learning Engineers, Data Scientists, and Platform/Software Engineers to architect scalable, maintainable, and systems that adhere to operational excellence principles. Core Areas of Responsibility Design, implement, and maintain MLOps pipelines for deploying, monitoring, and scaling machine learning models, including traditional models and LLM-powered applications. Ensure the architecture of ML systems prioritises scalability, reliability, and maintainability. Develop automated workflows for model training, testing, deployment, and monitoring. Implement monitoring and alerting systems to track model performance, data drift, and system health in production. Collaborate with Machine Learning Engineers and Data Scientists to refine model integration into production environments. Work with Platform/Software Engineers to integrate ML applications with existing infrastructure and ensure compatibility with cloud or on-premises systems. Stay up-to-date with MLOps best practices, tools, and new technologies to enhance system performance and reliability. About You 2+ years of experience MLOps, including deploying and maintaining machine learning models in production environments. Proficiency in Python programming and familiarity with ML frameworks such as TensorFlow, PyTorch, or equivalent. Experience with MLOps tools and platforms (e.g., MLflow, Kubeflow, Airflow, or similar). Knowledge of cloud platforms (e.g., AWS, Google Cloud, Azure) and containerization technologies (e.g., Docker, Kubernetes). Familiarity with CI/CD pipelines and version control systems like Git. Understanding of operational excellence principles, including system reliability, scalability, and monitoring.
