Lead Azure Cloud and AI Architect

Title: Lead Azure Cloud and AI Architect

Location: Hartford, CT(Remote)

Duration: 12+ Months

We are seeking an experienced Lead Azure Cloud & AI Architect to lead enterprise cloud architecture, AI/ML and Generative AI initiatives, and data platform modernization. The ideal candidate will define the technology vision and architecture roadmap, design scalable and secure Azure solutions, and drive cloud migrations, platform engineering, and enterprise data strategy.

This role requires deep expertise in Microsoft Azure, hands-on experience with AI and data platforms, and a strong track record of delivering enterprise-scale technology solutions while establishing architecture standards, governance, security, and compliance.

Key Responsibilities

• Enterprise Architecture: Lead the design and implementation of scalable, secure, highly available, and resilient enterprise solutions on Microsoft Azure.
• Azure Cloud & Platform Engineering: Drive Azure platform engineering, cloud-native architecture, infrastructure modernization, cloud migrations, and optimization of cloud environments.
• AI/ML & Generative AI: Architect and deliver enterprise AI/ML and Generative AI solutions using Azure AI services, Azure OpenAI, large language models (LLMs), Retrieval-Augmented Generation (RAG), and related technologies.
• Enterprise Data Strategy: Define and lead enterprise data architecture and modernization initiatives using Microsoft Fabric, Azure data platforms, data engineering, analytics, and business intelligence solutions.
• Architecture Standards & Governance: Establish enterprise architecture principles, reference architectures, technology standards, design patterns, and governance frameworks.
• Application & API Architecture: Guide the design of cloud-native applications, APIs, microservices, distributed systems, and integration architectures.
• DevOps & Automation: Promote DevOps, CI/CD, Infrastructure as Code (IaC), automation, and platform reliability practices.
• Security & Compliance: Ensure solutions align with enterprise security, identity and access management, data protection, regulatory compliance, and risk management requirements.
• Technology Roadmap: Own and drive the Azure, AI, and data architecture roadmap, evaluating emerging technologies and aligning technical investments with business objectives.
• Technical Leadership: Provide architectural guidance to engineering teams, collaborate with business and technology stakeholders, and mentor engineers and technical leads.

Required Qualifications

• 10+ years of experience in enterprise software engineering, cloud architecture, technology consulting, or solution architecture.
• Deep hands-on expertise in Microsoft Azure cloud services and enterprise architecture.
• Proven experience designing and implementing cloud-native, scalable, secure, and resilient enterprise solutions.
• Strong experience with AI/ML and Generative AI architectures, including Azure AI services, Azure OpenAI, LLMs, and RAG-based solutions.
• Experience with Microsoft Fabric and Azure data platforms, including enterprise data architecture, analytics, data integration, and governance.
• Strong understanding of microservices, REST APIs, distributed systems, and enterprise integration patterns.
• Experience with DevOps practices, CI/CD pipelines, Infrastructure as Code, and automated deployment strategies.
• Strong knowledge of cloud security, identity management, networking, compliance, and enterprise governance.
• Demonstrated ability to define technology roadmaps, influence architecture decisions, and lead cross-functional engineering teams.
• Excellent communication, stakeholder management, and technical leadership skills.

Preferred Qualifications

• Microsoft Azure certifications such as Azure Solutions Architect Expert or Azure AI Engineer Associate.
• Experience with Azure Kubernetes Service (AKS), containers, serverless architecture, and cloud-native application platforms.
• Knowledge of Azure Machine Learning, MLOps, model deployment, monitoring, and AI lifecycle management.
• Experience implementing enterprise data governance, data quality, metadata management, and access controls.
• Familiarity with responsible AI, AI security, and enterprise-scale Generative AI deployment patterns.