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
Build end-to-end ML pipelines including data ingestion training evaluation deployment and monitoring
Fine-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 optimization
Work with structured and unstructured data from sources such asAzure Data FactoryAzure Synapse AnalyticsAzure DatabricksAzure Data Lake Storage
Implement MLOps practices usingAzure DevOps GitHub ActionsCICD pipelines for ML models
Model versioning monitoring and retraining
Ensure data quality governance and lineage
Implement 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 mitigation
Collaborate with data scientists data engineers developers and business stakeholders
Translate business problems into AI-driven technical solutions
Provide technical guidance and mentor junior engineers
Support production issues and continuous improvement initiatives
Technical Skills
Strong experience with Microsoft Azure
Expertise in Azure AI ML services
Proficiency in Python (TensorFlow PyTorch Scikit-learn preferred)
Hands-on experience with REST APIs and microservices
Solid understanding of MLOps and CICD practices
Experience with containerization (Docker) and orchestration (AKS)
Knowledge of SQL and NoSQL data stores (Azure SQL Cosmos DB)
Azure AI Engineer Associate Certified (AI-102)