Insight Global is hiring several Cloud Engineering roles with one of our Financial clients in Charlotte NC. We are looking for candidates who have strong Cloud Engineering skills with either Azure and/or AWS and experience supporting Artificial Intelligence and Machine Learning initiatives. This role involves managing cloud infrastructure for machine learning workflows, deploying models using Azure and AWS Machine Learning, and ensuring high performance and availability of AI/ML systems. Desired Skills & Experience: • Experience with Azure AI/ML services: Azure ML, AKS, Azure Functions. • Proficient in Python and familiar with common ML libraries and tools. • Solid knowledge of Azure and AWS infrastructure, networking, and security. • Experience with ML lifecycle management tools like MLflow or Azure ML Studio. • Strong collaboration and troubleshooting skill • Strong hands-on experience with machine learning operations (MLOps) in cloud environments. • Proficient with Terraform for Infrastructure as Code (IaC). • Experience with AWS ML services like SageMaker, Step Functions, EKS, and Lambda. • Familiarity with the end-to-end ML lifecycle: data ingestion, model training, evaluation, deployment, and monitoring. • Proficient with GitHub, GitHub Actions, and Agile tools like Jira. • Excellent communication and stakeholder management skills. • AWS certifications, especially in Machine Learning or Solutions Architecture. • Experience in designing end-to-end cloud-native ML solutions. • Prior experience as a Solution Architect or working in an architecture-focused role. Key Responsibilities: • Build and maintain infrastructure to support ML workloads using Azure Machine Learning, AKS, and Azure Functions. • Collaborate with data science teams to deploy and monitor ML models. • Implement scalable infrastructure for training, inference, and model versioning. • Use tools like MLflow, Azure ML Pipelines, and Databricks for model management. • Automate infrastructure provisioning with Terraform or Bicep. • Ensure compliance with security, governance, and performance best practices. • Contribute to CI/CD and MLOps pipelines using GitHub Actions or Azure DevOps.