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

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

Location:  Hybrid; New York, NY (3 days onsite, 2 days remote)

Hours: 37.5/week (required 4 weeks unpaid vacation)

Duration: 6+ Month Contract, likely extension

Pay range: $78-$88.25/hr (W2), depending on experience

Interview: Could be onsite in New York, NY or remote via Teams (up to hiring manager). Likely 1-2 rounds of interviews

Our Government client in New York, NY is seeking a Senior Data & AI Solutions Architect – Azure & Generative AI to design, define, and deliver enterprise-scale data and artificial intelligence solutions across a large, complex, multi-department organization.

This is a hands-on architecture role combining deep technical expertise with significant business and stakeholder engagement. The architect will partner directly with business and operational leaders to uncover emerging or poorly defined business problems, translate them into clear technical and functional requirements, develop solution approaches, secure stakeholder approval, and help drive those solutions through implementation and production.

The ideal candidate is equally comfortable leading a whiteboard session with non-technical executives and working in the technical details of modern cloud data platforms, AI/ML solutions, generative AI, RAG architectures, databases, and agentic AI development workflows. Recent Azure experience is strongly preferred, although candidates must bring broader multi-cloud and multi-database architecture experience.

Key ResponsibilitiesData & 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 where appropriate, including Azure OpenAI and Azure Machine Learning.
• Leverage Copilot-style development tooling to accelerate prototyping and implementation.
• Help 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 such as Microsoft Fabric, Azure Synapse, Azure OpenAI, and Azure Machine Learning.
• Design solutions that operate 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.

Data Architecture & Modeling

• Design enterprise data environments spanning relational, NoSQL, analytical/warehouse, vector, and graph database technologies.
• Develop logical and physical data models supporting analytics and AI use cases.
• Design data ingestion, transformation, storage, and serving patterns.
• Prototype data solutions to validate architecture and business requirements.
• Support appropriate selection and use of database technologies based on specific workload requirements.
• Establish architecture patterns that support reliable, governed, and reusable enterprise data.

Requirements & Business Problem Definition

• Engage directly with stakeholders across business and operational departments to identify potential data and AI opportunities.
• Take ambiguous, emerging, or poorly defined problems and convert…