Azure ML Ops - Technical Lead / Solution Architect

We are seeking a seasoned Azure ML Ops Engineer to lead the design, deployment, and operationalization of machine learning solutions on the Azure platform. The ideal candidate will have deep expertise in MLOps practices, CI/CD for ML pipelines, and a strong understanding of Azure ML, DevOps, and containerized deployments. This role is critical to ensuring scalable, secure, and reliable ML model delivery in a global enterprise environment.
Key Responsibilities

• Design and implement CI/CD pipelines for ML model training, testing, and deployment using Azure DevOps and Azure ML.
  
• Manage the end-to-end lifecycle of ML models including versioning, monitoring, and retraining.
  
• Deploy and manage models using Azure ML endpoints, AKS, and containerized environments (Docker/Kubernetes).
  
• Collaborate with data scientists, data engineers, and architects to productionize ML solutions.
  
• Ensure compliance with enterprise standards for security, governance, and auditability.
  
• Monitor model performance, detect drift, and implement automated retraining workflows.
  
• Optimize infrastructure for cost, scalability, and performance.
  
• Maintain documentation and knowledge repositories for MLOps best practices.
  

Requirements

Required Qualifications

• Bachelor's or master's degree in computer science, Engineering, or a related field.
  
• 8+ years of experience in IT, with at least 3 years in MLOps or ML engineering roles.
  
• Proficiency in Azure ML, Azure DevOps, and cloud-native services.
  
• Strong programming skills in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
  
• Hands-on experience with Docker, Kubernetes, and infrastructure-as-code tools.
  
• Familiarity with MLFlow, model registries, and monitoring tools.
  
• Excellent problem-solving, communication, and collaboration skills.
  

Preferred Skills

• Azure certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect).
  
• Experience with GenAI, RAG (Retrieval-Augmented Generation), or LangChain-based architectures.
  
• Exposure to hybrid onshore-offshore delivery models.
  
• Knowledge of data privacy, compliance, and responsible AI practices.
  
• Experience with automated testing and validation of ML models.