• Strong proficiency in Python and FastAPI.

• Experience with MLOps tools and ML experimentation platforms such as MLflow (Runs & Experiments)

• Expertise in model versioning and lifecycle management (model training)

• Knowledge of ML frameworks/libraries (e.g., TensorFlow, PyTorch) and orchestration tools such as MLflow, Ray.io, Kubeflow, and Airflow

• Familiarity with Docker, Kubernetes, and microservice-based architectures

• Experience with tools such as MLflow, LakeFS, MinIO, and NATS for asynchronous communication

• Knowledge of database design and best practices

• Understanding of RBAC and authentication mechanisms