Python + ML + MLOps

Job description

Python + Machine Learning + MLOps Engineer We are seeking a skilled **Python + Machine Learning + MLOps Engineer** to design, build, and deploy scalable machine learning solutions across enterprise environments. You will develop end-to-end ML pipelines, operationalize models in production, and collaborate with cross-functional teams to deliver reliable, high-performance AI solutions. - Design, develop, and deploy machine learning models and data-driven solutions using Python. - Build and maintain end-to-end ML pipelines for data ingestion, training, validation, deployment, and monitoring. - Develop scalable and production-ready machine learning systems following MLOps best practices. - Optimize model performance, reliability, and scalability across cloud and on-premise environments. - Collaborate with data scientists, data engineers, and business stakeholders to translate requirements into ML solutions. - Implement automated model training, testing, deployment, and monitoring workflows. - Establish CI/CD pipelines for machine learning applications and model lifecycle management. - Monitor model drift, data quality, and production performance to ensure continuous improvement. - Contribute to code reviews, technical documentation, and engineering best practices. - Strong Python programming skills with hands-on experience in machine learning development. - Experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar. - Hands-on experience with MLOps tools such as MLflow, Kubeflow, Airflow, DVC, or SageMaker. - Experience with containerization and orchestration technologies such as Docker and Kubernetes. Good to Have - Experience with cloud platforms including Azure, AWS, or Google Cloud Platform. - Knowledge of DevOps practices, Infrastructure as Code (Terraform, Ansible), and CI/CD tools. - Exposure to Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). - Experience with big data technologies such as Spark, Hadoop, or Databricks. - Understanding of model governance, responsible AI practices, and ML security. **Naukri Mapping** - **Department:** Engineering - Software & QA - **Role Category:** Data Science & Machine Learning - **Role:** Machine Learning Engineer / MLOps Engineer / Senior ML Engineer