MLOps Engineer

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.