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.