Associate MLOps & Cloud AI Engineer

Job purpose Design, develop, and manage scalable MLOps pipelines for building, training, and deploying predictive and generative LLM models on cloud infrastructure. Design, develop, and manage scalable MLOps pipelines for building, training, and deploying predictive and generative LLM models on cloud infrastructure. The role supports automation, monitoring, and optimization of AI workflows across research, clinical, and commercial initiatives to enable data-driven decision-making and operational efficiency. Duties and responsibilities The successful candidate will work closely with R&D and Corporate stakeholders under Ocugens R&D and Business organization. Design, develop, and maintain end-to-end MLOps pipelines on Azure for training, evaluating, and deploying AI and LLM-based models across multiple environments. Integrate and manage Azure OpenAI Service, Azure AI Search, Azure Document Intelligence, and Azure Blob Storage for model orchestration, data ingestion, and large-scale retrieval workflows. Build and manage CI/CD pipelines using Azure DevOps or GitHub Actions to automate code builds, testing, model deployment, and versioning. Implement infrastructure-as-code for reproducible environment setup using tools like Bicep or Terraform , ensuring consistency across R&D and production environments. Configure Azure Container Apps, Container Registry, and Kubernetes clusters to support scalable and fault-tolerant model deployment. Develop data pipelines for ingesting, cleaning, and indexing structured and unstructured data into Azure Blob and Azure AI Search for downstream AI workloads. Monitor and optimize model inference performance, latency, and cost , leveraging Azure Monitoring, Application Insights, and autoscaling strategies. Track and manage token utilization and billing for Azure OpenAI and other AI services to ensure efficient resource consumption. Implement secure storage and access control using Azure Key Vault, role-based access, and compliance policies. Collaborate with AI, backend, and DevOps teams to deploy and maintain LLM and data analytics models integrated with enterprise APIs and dashboards. .