Senior MLOps Engineer

The MLOps Engineer is responsible for designing, implementing, and maintaining scalable, reproducible, and auditable machine learning pipelines. This role bridges the gap between data science, engineering, and operations by enabling reliable deployment, monitoring, and governance of machine learning models in production environments. Responsibilities include developing and maintaining end-to-end ML pipelines using Kedro, managing experiment tracking and model versioning with MLflow, deploying models using ArgoCD and Kubernetes (EKS), and setting up observability and performance monitoring using the Grafana + Prometheus stack. The role also involves automating the full ML lifecycle using CI/CD pipelines integrated with AWS and handling artifact and dependency management using JFrog Artifactory.