We are seeking a MLOps Engineer to lead the design and implementation of a scalable, data-intensive, production-grade ML system. You will own the ML deployment lifecycle—from model versioning, deployment, and performance monitoring to CI/CD automation and retraining workflows. This role demands deep cloud (preferably AWS), strong Python and ML libraries proficiency (PyTorch, Scikit-learn), and a solid foundation in software engineering. You will collaborate cross-functionally with product, Data Science and Data Engineering teams.
Skills and Requirements
• Strong knowledge of MLOps principles, model deployment, versioning, monitoring, and retraining.
• Proficiency in Python and ML frameworks (PyTorch, Scikit-learn, XGBoost).
• Experience with CI/CD tools (e.g., Jenkins, GitHub Actions).
• Expertise in cloud platforms, preferably AWS.
• Experience working with ML training/inference platforms such as Databricks, and Cloudera.
• Familiarity with MLOps tools (e.g., MLflow, Kubeflow) and cloud-native deployment
• Previous experience with ML model deployment, monitoring and maintenance
• Proficiency in working with Apache Spark (PySpark, Scala, or Java), Hadoop, and Kafka for building
distributed data processing pipelines.
• Strong communication and cross-functional collaboration skills.
• Highly organised, autonomous, and ownership-driven.