Azure AI Architect

Key Responsibilities

• Architect and implement intelligent applications using Azure AI services including Azure OpenAI, Azure Cognitive

Services, and Azure Machine Learning.

• Lead AI experimentation initiatives to evaluate models, configurations, and deployment strategies aligned with

business goals.

• Embed AI capabilities such as NLP, computer vision, speech recognition, and decision-making into enterprise

applications.

• Integrate AI models with internal and external systems (web, mobile, APIs) ensuring seamless interaction with

business logic.

• Design and implement scalable, low-latency, and high-availability AI components using Azure App Services, AKS,

and serverless patterns.

• Provision and manage Azure AI Foundry workspaces, compute environments, and model endpoints.
• Implement secure access controls, RBAC, and data governance policies to ensure compliance and responsible AI

practices.

• Guide development teams on MLOps best practices including model versioning, CI/CD pipelines, monitoring, and

drift detection.

• Collaborate with business stakeholders to identify opportunities for AI-led process optimization and

transformation.

• Contribute to internal AI strategy committees and innovation forums to shape enterprise AI adoption.
• Document architecture patterns, reusable components, and integration blueprints for AI applications.
• Actively participate in hands-on coding and development of AI solutions.

Required Skills

• Azure AI & ML
• Azure AI Foundry: Workspace provisioning, model lifecycle management
• Azure OpenAI: Prompt engineering, model fine-tuning, endpoint management
• Azure Cognitive Services: Vision, Speech, Language, and Decision APIs
• Azure Machine Learning: Training, deployment, monitoring, and MLOps
• Experience with LLM orchestration and prompt chaining
• Familiarity with Azure AI Studio and Azure Arc
• Proficiency in using Azure AI SDKs within custom applications
• Hands-on experience in at least one AI/ML domain (Computer Vision, Automation, Predictive Analytics,

RPA, etc.) with relevant libraries (Pandas, TensorFlow, Scikit-learn, etc.)

• Application Integration
• Programming: Python, .NET, JavaScript/TypeScript
• Integration: REST APIs, Azure Functions, Logic Apps, Event Grid
• Architecture: Microservices, serverless, event-driven, real-time & batch inference pipelines
• Infrastructure: Azure App Services, AKS, API Management
• Experience with containerized AI workloads (Docker, Kubernetes)
• Security & Governance
• RBAC, Managed Identities, Private Endpoints
• Data privacy, encryption, and responsible AI principles

Preferred Skills

• Exposure to multi-cloud AI deployment strategies.
• Knowledge of AI-driven business process automation and Agentic AI frameworks.

Regards,

Sumedha