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

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

Location: Broadway, NY

Rate: $80/hour on W2

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

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

Key Responsibilities

Data & AI Solution Architecture

• Architect end-to-end enterprise data and AI solutions spanning data ingestion, storage, transformation, modeling, serving, integration, and consumption.
• Design scalable architectures supporting traditional analytics, machine learning, generative AI, and emerging AI use cases.
• Develop data models, prototypes, solution architectures, and implementation approaches.
• Define reference architectures, reusable patterns, and technical standards for enterprise data and AI solutions.
• Design solutions across multi-cloud and multi-database environments.
• Evaluate architectural alternatives and clearly articulate technical trade-offs, risks, costs, and business value.
• Ensure solutions are scalable, maintainable, secure, and production-ready.

Generative AI & Agentic Development

• Design and implement solutions leveraging generative AI, LLMs, RAG, model orchestration, and AI/ML frameworks.
• Use agentic development tooling and AI-assisted development workflows to accelerate solution delivery.
• Evaluate appropriate AI models, frameworks, platforms, and architectural patterns for specific business problems.
• Develop solutions using Microsoft AI technologies, including Azure OpenAI and Azure Machine Learning.
• Leverage Copilot-style development tooling to accelerate prototyping and implementation.
• Establish effective enterprise patterns for responsible and maintainable AI adoption.

Azure & Multi-Cloud Architecture

• Architect modern data and AI solutions with a strong emphasis on the Microsoft Azure ecosystem.
• Work with Azure data services and platforms including Microsoft Fabric, Azure Synapse, Azure OpenAI, and Azure Machine Learning.
• Design solutions across multiple cloud providers, including Azure, AWS, and/or GCP.
• Evaluate cloud services and architecture patterns based on business, technical, security, performance, and cost requirements.
• Support cloud architecture decisions across data engineering, analytics, AI/ML, and application integration use cases.

Required Qualifications

• 8+ years of overall experience in data engineering, data architecture, software engineering, or a closely related field.
• 6+ years of experience in architecture-focused roles.
• 6+ years of information architecture experience.
• 6+ years of data analysis experience.
• 4–6+ years of data modeling and prototyping experience.
• 4–6+ years of experience designing data environments.
• 4–6+ years of hands-on AI experience.
• Strong experience analyzing and synthesizing complex qualitative and quantitative information.
• Demonstrated ability to solve complex and ambiguous business and technology problems.
• Hands-on architecture and delivery experience across at least two major cloud platforms, such as Azure, AWS, and GCP.
• Strong Azure experience, including modern Azure data and AI services.
• Experience with Microsoft Fabric, Azure Synapse, Azure OpenAI, and/or Azure Machine Learning.
• Broad database experience spanning relational, NoSQL, analytical/data warehouse, vector, and/or graph technologies.
• Practical experience across multiple AI/ML and generative AI technologies, including LLMs, RAG, ML pipelines, and model orchestration.
• Hands-on experience with agentic development tooling and AI-assisted development workflows.
• Demonstrated ability to translate ambiguous business problems into clear, actionable technical requirements.
• Experience developing and presenting solution architectures to technical and non-technical stakeholders.
• Demonstrated experience taking solutions from concept and stakeholder approval through production implementation.
• Strong knowledge of data architecture, modeling, prototyping, and enterprise data environments.
• Strong understanding of cloud architecture, data engineering, analytics, AI/ML, and application integration.
• Excellent written, verbal, presentation, and stakeholder communication skills.