AI Architect

AI Architect

The ideal candidate combines strong technical leadership with hands-on experience architecting end-to-end AI/ML systems—from data readiness through model deployment—leveraging Azure’s cloud-native and Fabric-based services.

Key Responsibilities

• Architect and lead the design and implementation of AI/ML solutions on Azure Cloud (Azure Machine Learning, Azure Databricks, Synapse, Azure AI Foundry, Microsoft Fabric, Cognitive Services, Azure OpenAI etc.).
• Define end-to-end AI architecture encompassing data pipelines, feature stores, model training, deployment, and monitoring.
• Partner with stakeholders to translate business challenges into AI-driven solutions and technical blueprints.
• Design scalable and secure architectures adhering to best practices in data governance, MLOps, LLMOps, and cost optimization.
• Integrate Microsoft Fabric as the unified data foundation for AI workloads, ensuring governed, high-quality data access and lineage visibility.
• Lead MLOps initiatives including model CI/CD, versioning, monitoring, and drift detection using Azure DevOps, Azure ML Pipelines, and Azure AI Foundry.
• Evaluate and implement Generative AI (GenAI) and LLM-based solutions using Azure OpenAI, Cognitive Services, and frameworks such as LangChain or LangGraph.
• Establish observability and monitoring frameworks using Azure Monitor, Application Insights, MLflow, and Databricks dashboards for AI workloads.
• Collaborate with cross-functional teams—Data Engineers, Data Scientists, and DevOps—to ensure seamless integration and delivery of AI products.
• Provide technical mentorship and architectural guidance to engineering teams.
• Stay current with emerging trends in AI/ML, cloud technologies, and GenAI advancements.

Qualifications

• 10 + years of overall IT experience, including 5 + years in AI/ML solutioning and 3 + years in Azure-based architecture.
• Deep expertise in the Azure AI ecosystem—Azure Machine Learning, Azure Databricks, Azure AI Foundry, Microsoft Fabric, Azure Cognitive Services, Azure OpenAI, Azure Synapse, and Data Lake.
• Strong proficiency in Python and major ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Proven experience in MLOps and LLMOps design and automation using Azure DevOps, Docker, and Kubernetes.
• Hands-on experience integrating AI solutions with Microsoft Fabric and Azure Data Factory for data preparation and governance.
• Strong understanding of distributed systems, model lifecycle management, and AI system scalability.
• Experience in LLM fine-tuning, prompt engineering, or AI solution integration with enterprise applications.
• Excellent communication and stakeholder management skills with a strategic mindset.