Azure Machine Learning - S

Key Responsibilities

• Design, develop, and deploy machine learning models and AI solutions using Azure services.
• Build and maintain end-to-end ML pipelines for data preparation, training, validation, deployment, and monitoring.
• Develop and manage MLOps practices, including model versioning, automation, and CI/CD processes.
• Collaborate with data scientists to operationalize machine learning models.
• Deploy and manage models using Azure Machine Learning and Azure Kubernetes Service (AKS).
• Monitor model performance and implement retraining strategies as needed.
• Optimize machine learning solutions for scalability, performance, and cost-efficiency.
• Integrate AI/ML services into enterprise applications and business processes.
• Ensure compliance with data governance, security, and regulatory requirements.
• Document technical designs, workflows, and deployment procedures.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
• 3+ years of experience in Machine Learning, Data Science, or AI solution development.
• Hands-on experience with Azure Machine Learning (Azure ML).
• Strong programming skills in Python.
• Experience with machine learning frameworks such as:
• Scikit-learn
• TensorFlow
• PyTorch
• XGBoost
• Knowledge of supervised and unsupervised learning techniques.
• Experience in feature engineering, model evaluation, and model deployment.
• Familiarity with REST APIs and integrating ML services into applications.
• Strong understanding of data processing and ETL concepts.

Azure Skills

• Azure Machine Learning Studio
• Azure AI Services / Azure Cognitive Services
• Azure OpenAI (preferred)
• Azure Data Factory
• Azure Databricks
• Azure Synapse Analytics
• Azure Blob Storage
• Azure Data Lake Storage
• Azure Kubernetes Service (AKS)
• Azure Container Registry (ACR)
• Azure Key Vault
• Azure Monitor

MLOps & DevOps Skills

• CI/CD implementation using Azure DevOps or GitHub Actions
• ML model lifecycle management
• Model registry and version control
• Containerization using Docker
• Kubernetes orchestration
• Infrastructure as Code (Terraform/Bicep preferred)