Data Scientist & AI

This role involves designing and implementing ML pipelines focusing on MLOps and LLMOps principles, including deploying large language models and optimizing performance. Responsibilities include managing Azure-based workflows, Kubernetes clusters, and tools like MLflow, Terraform, and Azure DevOps. Key tasks cover CI/CD pipelines, monitoring solutions, and maintaining training data and experiment tracking systems.

Responsibilities:

• Design and implement end-to-end ML pipelines using Azure Machine Learning service and MLflow- Build and maintain infrastructure for training, fine-tuning, and deploying large language models
• Implement monitoring solutions for model performance, drift detection, and operational metrics
• Orchestrate ML workflows using Azure Machine Learning pipelines and Azure Functions
• Optimize model serving infrastructure for cost and performance
• Implement feature stores and manage training data pipelines
• Set up experiment tracking and model registry systems
• Ensure ML systems comply with security and governance requirements
• Design, implement, and maintain CI/CD pipelines using Azure DevOps and Azure Pipelines
• Manage Infrastructure as Code using Terraform
• Configure and maintain Kubernetes clusters using AKS (Azure Kubernetes Service)

Requirements:

• 5+ years of experience in MLOPs
• 3+ years of experience with Azure cloud services and Azure DevOps
• Strong understanding of MLOps/LLMOps principles and machine learning lifecycle management
• Proficiency in Python and shell scripting
• Experience with containerization technologies (Docker, Kubernetes)
• Knowledge of Infrastructure as Code tools (Terraform)
• Understanding of CI/CD principles and implementation
• Experience with monitoring and observability tools
• Strong security awareness and experience implementing security best practices