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Role Descriptions: Design and implement AIML solutions using Azure AI services such asAzure OpenAIAzure Machine LearningAzure Cognitive Services (Vision| Language| Speech| Decision)Develop and deploy ML models using Python| R| or .NETBuild end-to-end ML pipelines including data ingestion| training| evaluation| deployment| and monitoringFine-tune and manage LLMs and generative AI solutions where applicable.Architect cloud-native AI solutions leveragingAzure ML WorkspacesAzure FunctionsAzure App ServicesAzure Kubernetes Service (AKS)Implement AI model deployment patterns (batch| real-time| and event-driven inference)Ensure scalability| resiliency| performance| and cost optimizationWork with structured and unstructured data from sources such asAzure Data FactoryAzure Synapse AnalyticsAzure DatabricksAzure Data Lake StorageImplement MLOps practices usingAzure DevOps GitHub ActionsCICD pipelines for ML modelsModel versioning| monitoring| and retrainingEnsure data quality| governance| and lineageImplement Azure security best practicesManaged identitiesKey VaultRole-Based Access Control (RBAC)Design AI systems compliant with enterprise and regulatory standards (e.g.| BFSI| HIPAA| PII| GDPR)Apply responsible AI principles fairness| explainability| transparency| and bias mitigationCollaborate with data scientists| data engineers| developers| and business stakeholdersTranslate business problems into AI-driven technical solutionsProvide technical guidance and mentor junior engineersSupport production issues and continuous improvement initiativesTechnical SkillsStrong experience with Microsoft AzureExpertise in Azure AI ML servicesProficiency in Python (TensorFlow| PyTorch| Scikit-learn preferred)Hands-on experience with REST APIs and microservicesSolid understanding of MLOps and CICD practicesExperience with containerization (Docker) and orchestration (AKS)Knowledge of SQL and NoSQL data stores (Azure SQL| Cosmos DB)Azure AI Engineer Associate Certified (AI-102)