Description

Key Responsibilities

• Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI
• Automate model training, testing, deployment, and monitoring in cloud environments (GCP, AWS, Azure)
• Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining
• Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
• Leverage AutoML tools (Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development and rapid deployment

Required Skills

• Strong proficiency in Java, Python, and SQL
• ML libraries: scikit-learn, XGBoost, TensorFlow, PyTorch
• Cloud platforms: GCP, AWS, Azure
• Containerization: Docker, Kubernetes
• Data engineering tools: Airflow, Spark
• ML Ops frameworks and DevOps practices

Key Skills

Education

Any Gradute