AI Architect – Microsoft Azure
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AI Architect – Microsoft Azure
Primary Skills: Microsoft Azure, Azure AI, Azure Machine Learning, Azure OpenAI, Python, MLOps, Cloud Architecture
Skill Level: Master
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Role Overview
AI Architect responsible for designing and implementing enterprise-grade AI, machine learning, and cloud-native solutions on Microsoft Azure. The role focuses on defining scalable AI architectures, enabling advanced analytics and generative AI capabilities, establishing MLOps best practices, and delivering secure, high-performing AI platforms aligned with business objectives. The ideal candidate combines deep Azure expertise with strong architectural, technical leadership, and stakeholder management skills.
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Key Responsibilities
- Design and implement enterprise AI and machine learning architectures on Microsoft Azure
- Lead architecture decisions for AI platforms, data pipelines, model deployment, and MLOps
- Design scalable cloud-native AI solutions using Azure services and modern engineering practices
- Architect solutions leveraging Azure AI Studio, Azure Machine Learning, Azure OpenAI Service, and Cognitive Services
- Design and optimize enterprise data platforms using Azure Data Lake, Azure Synapse Analytics, and Azure Databricks
- Define architecture standards for governance, security, compliance, monitoring, and cost optimization
- Design APIs and integration frameworks for AI-enabled applications and enterprise services
- Collaborate with data scientists, data engineers, and software development teams to operationalize machine learning models
- Design and support generative AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector search, and conversational AI
- Lead architecture workshops, technical reviews, and stakeholder discussions
- Produce solution blueprints, architecture documentation, and implementation roadmaps
- Evaluate emerging Azure AI technologies and recommend strategic improvements
- Mentor engineering teams and promote architecture best practices across AI initiatives
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Required Skills
- Strong experience designing enterprise AI and cloud architectures on Microsoft Azure
- Hands-on experience with Azure AI services, including:
- Azure AI Studio
- Azure Machine Learning
- Azure OpenAI Service
- Azure Cognitive Services
- Experience designing and deploying machine learning solutions in enterprise environments
- Strong knowledge of Azure data services, including:
- Azure Data Lake Storage
- Azure Synapse Analytics
- Azure Databricks
- Proficiency in Python and SQL
- Strong understanding of MLOps, CI/CD pipelines, infrastructure automation, and model lifecycle management
- Experience with containerization and orchestration technologies:
- Docker
- Kubernetes
- Experience designing REST APIs, microservices, and distributed systems
- Strong understanding of Azure security, identity management, and governance
- Excellent analytical, problem-solving, communication, and stakeholder management skills
- Ability to lead technical discussions and architecture decision-making
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Nice to Have
- Microsoft Azure certifications, including:
- Azure Solutions Architect Expert
- Azure AI Engineer Associate
- Azure Data Engineer Associate
- Experience designing Generative AI solutions using Azure OpenAI Service
- Knowledge of vector databases, semantic search, embeddings, and Retrieval-Augmented Generation (RAG)
- Experience with Infrastructure as Code (IaC) tools such as Terraform or Bicep
- Experience working in regulated industries such as healthcare, financial services, or government
- Familiarity with DevOps practices and Agile delivery methodologies
- Experience with GitHub Actions or Azure DevOps pipelines
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Candidate Profile
Experienced AI Architect with deep expertise in Microsoft Azure, enterprise AI platforms, and cloud-native solution architecture. Proven ability to design scalable, secure, and production-ready AI and machine learning solutions while collaborating with cross-functional teams and business stakeholders. Strong background in Azure AI services, data engineering, MLOps, and generative AI technologies, with the ability to define architecture strategy, mentor technical teams, and drive innovation across enterprise AI initiatives.