Sr. Data & AI Solutions Architect - Azure & Generative AI

Job Description

Job title: Sr. Data & AI Solutions Architect - Azure & Generative AI

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Location: Broadway, NY

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Rate: $80/hour on W2

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Type: Contract – Hybrid (3 days onsite, 2 days remote)

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Must: 8+ years of overall experience, 6+ years of architecture-focused experience, hands-on Azure experience, Generative AI/LLM/RAG experience, and multi-cloud architecture experience

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Key Responsibilities

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Data & AI Solution Architecture

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• Architect end-to-end enterprise data and AI solutions spanning data ingestion, storage, transformation, modeling, serving, integration, and consumption.

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• Design scalable architectures supporting traditional analytics, machine learning, generative AI, and emerging AI use cases.

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• Develop data models, prototypes, solution architectures, and implementation approaches.

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• Define reference architectures, reusable patterns, and technical standards for enterprise data and AI solutions.

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• Design solutions across multi-cloud and multi-database environments.

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• Evaluate architectural alternatives and clearly articulate technical trade-offs, risks, costs, and business value.

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• Ensure solutions are scalable, maintainable, secure, and production-ready.

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Generative AI & Agentic Development

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• Design and implement solutions leveraging generative AI, LLMs, RAG, model orchestration, and AI/ML frameworks.

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• Use agentic development tooling and AI-assisted development workflows to accelerate solution delivery.

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• Evaluate appropriate AI models, frameworks, platforms, and architectural patterns for specific business problems.

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• Develop solutions using Microsoft AI technologies, including Azure OpenAI and Azure Machine Learning.

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• Leverage Copilot-style development tooling to accelerate prototyping and implementation.

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• Establish effective enterprise patterns for responsible and maintainable AI adoption.

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Azure & Multi-Cloud Architecture

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• Architect modern data and AI solutions with a strong emphasis on the Microsoft Azure ecosystem.

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• Work with Azure data services and platforms including Microsoft Fabric, Azure Synapse, Azure OpenAI, and Azure Machine Learning.

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• Design solutions across multiple cloud providers, including Azure, AWS, and/or GCP.

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• Evaluate cloud services and architecture patterns based on business, technical, security, performance, and cost requirements.

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• Support cloud architecture decisions across data engineering, analytics, AI/ML, and application integration use cases.

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Required Qualifications

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• 8+ years of overall experience in data engineering, data architecture, software engineering, or a closely related field.

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• 6+ years of experience in architecture-focused roles.

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• 6+ years of information architecture experience.

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• 6+ years of data analysis experience.

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• 4–6+ years of data modeling and prototyping experience.

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• 4–6+ years of experience designing data environments.

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• 4–6+ years of hands-on AI experience.

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• Strong experience analyzing and synthesizing complex qualitative and quantitative information.

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• Demonstrated ability to solve complex and ambiguous business and technology problems.

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• Hands-on architecture and delivery experience across at least two major cloud platforms, such as Azure, AWS, and GCP.

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• Strong Azure experience, including modern Azure data and AI services.

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• Experience with Microsoft Fabric, Azure Synapse, Azure OpenAI, and/or Azure Machine Learning.

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• Broad database experience spanning relational, NoSQL, analytical/data warehouse, vector, and/or graph technologies.

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• Practical experience across multiple AI/ML and generative AI technologies, including LLMs, RAG, ML pipelines, and model orchestration.

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• Hands-on experience with agentic development tooling and AI-assisted development workflows.

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• Demonstrated ability to translate ambiguous business problems into clear, actionable technical requirements.

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• Experience developing and presenting solution architectures to technical and non-technical stakeholders.

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• Demonstrated experience taking solutions from concept and stakeholder approval through production implementation.

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• Strong knowledge of data architecture, modeling, prototyping, and enterprise data environments.

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• Strong understanding of cloud architecture, data engineering, analytics, AI/ML, and application integration.

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• Excellent written, verbal, presentation, and stakeholder communication skills.

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