Required Skills
Robust programming skills in Python.
Hands-on experience with MLOps pipelines and machine learning lifecycle management.
Experience with Docker and Kubernetes.
Knowledge of CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
Experience with MLflow, Kubeflow, Airflow, or equivalent orchestration and lifecycle tools.
Knowledge of cloud-based ML services such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
Understanding of REST APIs, model serving, automated testing, and deployment strategies.
Familiarity with Git, Linux, and infrastructure-as-code tools.
Understanding of model monitoring, data quality, model drift, and performance optimization.
Valuable to Have
Experience with feature stores and model registries.
Knowledge of PyTorch, TensorFlow, or Scikit-learn.
Experience with LLMOps, GenAI deployment, and LLM monitoring.
Familiarity with Spark, Kafka, and large-scale data processing.
Experience with security, governance, and compliance in production ML systems