MLOps Engineer

Skills:- Mlops, Python, Terraform, Docker, Kubernetes, CICD Pipelines, Mlflow/Kubeflow/Airflow, (AzureML, Sagemaker, Skills & Qualifications

Programming Mastery: Strong proficiency in Python (and/or Java depending on customer confirmation) with an emphasis on writing clean, production-grade, object-oriented code.

Data Science Capabilities: Solid understanding of machine learning fundamentals, model evaluation metrics, hyperparameter tuning, and frameworks (e.g., scikit-learn, TensorFlow, PyTorch).

ML Infra & Orchestration: Experience with ML pipelines and orchestration tools (e.g., MLflow, Kubeflow, Airflow) and data versioning (DVC).

DevOps Foundation: Hands-on experience with containerization (Docker, Kubernetes) and Cloud Infrastructure (AWS, Azure, or GCP).

Analytical Mindset: Exceptional root-cause analysis and debugging skills-the ability to trace an issue from a bad data input all the way through to a skewed model prediction.

(ref:hirist.tech)