MLOps Engineer

We are seeking an MLOps Engineer to build operational deployment pipelines, model tracking systems, and feature store infrastructure for ML teams. Tech stack: Python, Kubernetes, MLflow, CI/CD, Docker, AWS, Feature Stores, SQL What you will work on: - Build and maintain automated machine learning pipelines covering model training, testing, and deployment. - Implement continuous model monitoring systems for tracking model performance, latency, and data drift. - Manage MLOps platform tools (MLflow, Kubeflow) running on top of Kubernetes clusters. - Collaborate with data science and software engineering teams to deploy models into production. What we are looking for: - Practical experience managing machine learning infrastructure, model registries, and automated pipelines. - Proficiency programming in Python alongside hands-on knowledge of Docker, Kubernetes, and cloud services. - Understanding of both software engineering practices and ML model development lifecycles.