Manager Azure AI Engineer

Job Description
In This Role, Your Responsibilities Will Be:

• As an Azure AI Engineer, you will be responsible for architecting and implementing end-to-end AI solutions using Azure Machine Learning, Azure AI Studio, Azure AI Foundry, Cognitive Services, and the Power Platform. You will leverage both prebuilt AI models and custom machine learning workflows to solve real business problems, integrate AI into enterprise systems, and ensure robust governance, monitoring, and lifecycle management.

• An Azure AI Engineer who can design, build, and operate intelligent solutions using Microsoft Azure’s AI and machine learning ecosystem. The ideal candidate combines strong technical expertise with a practical understanding of scalable, secure, and responsible AI development. While not every listed skill is mandatory, a solid mix of the capabilities below will position you for success in this role.

• AI Solution Design & Development
• Build, train, and deploy machine learning models using Azure Machine Learning, Python, Azure AI Foundry and Azure ML SDKs.
• Develop scalable AI pipelines using Azure ML pipelines, AutoML, and cloud-native best practices.
• Integrate Azure AI capabilities—Vision, Language, Speech, and Document Intelligence—into enterprise applications.
• Design and automate AI-driven solutions using the Power Platform (Power Apps, Power Automate, Power Virtual Agents) integrated with Azure AI Services.
• Utilize Azure AI Studio, Azure AI Foundry to rapidly prototype and deliver generative AI, agentic AI and cognitive solutions.

Cross-Functional Collaboration

• Partner with data engineers, analytics teams, and business stakeholders to translate business needs into impactful AI solutions.
• Collaborate with Power Platform developers to embed AI models and cognitive services into low-code applications and workflows.
• Contribute to architecture decisions ensuring scalability, maintainability, and alignment with enterprise cloud standards.
• Communicate technical concepts clearly to both technical and non-technical audiences.

Responsible AI, Security & Governance

• Apply Responsible AI guidelines, ensuring fairness, explainability, and ethical use of machine learning models.
• Enforce data privacy, compliance, and security standards across all AI workloads.
• Support model versioning, governance, and lifecycle management.

MLOps, Monitoring & Optimization

• Implement monitoring and observability for AI workloads using Azure Monitor, Application Insights, and MLflow (where applicable).
• Optimize model performance, reliability, and cost efficiency across training and inference workloads.
• Contribute to CI/CD workflows for ML and Power Platform integrations using Azure Dev Ops or Git Hub Actions.

• Bachelor’s degree in computer science, Computer Engineering, IT, or related discipline.
• Hands-on experience designing and deploying AI/ML solutions on Microsoft Azure.
• Experience with Azure AI Studio, Azure AI Foundry, Azure ML Designer, AutoML, and Prompt engineering Flow.
• Strong proficiency in Python and Azure SDKs for ML and Cognitive Services.
• Solid understanding of machine learning concepts: data preparation, model development, evaluation, and deployment.
• Experience with cloud-native development and CI/CD for ML pipelines.
• Strong communication, collaboration, and problem-solving skills.

• Exposure to Microsoft Power Platform and integrating Azure AI models/services into Power Apps, Power Automate, and Copilots.
• Familiarity with MLOps practies and tools such as MLflow, Azure Dev Ops, or Git Hub Actions.
• Microsoft certifications (e.g., Azure AI Engineer Associate, Azure Data Scientist Associate) are an added advantage.
• Working knowledge of Power BI for embedding or visualizing AI-driven insights.