Description

You will manage the end-to-end lifecycle of machine learning models from deployment to continuous monitoring.

Responsibilities

• Deploy trained models into production environments for both batch and real-time processing.
• Automate training pipelines, retraining workflows, and ML-specific CI/CD pipelines.
• Manage infrastructure using containerization, orchestration, and Infrastructure as Code.
• Monitor model performance, track data drift, and maintain logging and alerting systems.
• Maintain model and dataset versioning to ensure reproducible environments.

Required Skills

• 5+ years of experience in MLOps or related roles.
• Proficiency in Python and ML libraries including scikit-learn, PyTorch, and TensorFlow.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Hands-on expertise with Docker, Kubernetes, and Terraform.
• Strong knowledge of orchestration tools like Airflow, Azure ML Pipelines, MLflow, or Jenkins.
• Experience deploying models via REST APIs using FastAPI or Flask.
• Familiarity with model packaging formats such as ONNX or TorchScript.
• Competency in SQL, ETL processes, and data formats like Delta Lake or Parquet.
• Proficiency with Git and DVC for version control.

Preferred Skills

• Experience with Azure Data Factory for data pipeline orchestration.
• Ability to write production-ready code with disciplined error handling and logging.

Key Skills

Education

Any Graduate