Senior Azure Artificial Intelligence Engineer

Senior Azure AI Engineer

Azure Machine Learning, Azure Cognitive Services, Azure OpenAI Service, Prompt Engineering, MLOps • Design, develop, and integrate AI solutions using Azure AI services aligned to business requirements. • Build and iterate AI prototypes and workflows using Azure AI Studio, improving solution quality through experimentation and evaluation. • Implement end-to-end AI pipelines including data preparation, model/service selection, prompt/workflow design (where applicable), and deployment to Azure environments. • Collaborate with cross-functional teams to define use cases, success metrics, and acceptance criteria for AI features. • Ensure solutions meet performance, scalability, security, and compliance expectations in cloud deployments. • Monitor and troubleshoot AI solutions in production, driving continuous improvement through feedback loops and iterative releases. • Document architectures, workflows, and operational runbooks to support maintainability and knowledge sharing. Minimum Qualifications: • Bachelor’s degree in B.Tech (or equivalent) in Computer Science, IT, Engineering, or a related field. • 2–5 years of experience delivering software or cloud solutions with hands-on exposure to Azure AI services. • Working experience with Azure AI Studio for building, testing, and refining AI solutions/workflows. • Strong understanding of cloud fundamentals, APIs/integration patterns, and production-grade engineering practices. • Ability to communicate clearly with technical and non-technical stakeholders and work effectively in a team setting. Preferred Qualifications: • Experience designing solution architectures for AI workloads on Azure, including environment setup, deployment patterns, and operational considerations. • Familiarity with responsible AI practices such as safety, privacy, bias considerations, and governance controls in enterprise settings. • Experience with evaluation approaches for AI solutions (quality, latency, cost), and iterative optimization based on measurable outcomes. • Exposure to CI/CD and automation for deploying and maintaining AI solutions in Azure. • Proven ability to translate ambiguous problem statements into implementable AI workflows and deliver incrementally with stakeholders.