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 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 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 them into well-scoped business and technology initiatives.
• Facilitate requirements-gathering and discovery sessions with technical and non-technical stakeholders.
• Analyze and synthesize qualitative and quantitative information to identify underlying needs.
• Develop clear, actionable requirements and specifications that both business and technical audiences can understand.
• Ensure proposed solutions directly address the underlying operational or business problem.

Stakeholder Engagement & Solution Approval

• Present proposed architectures, solution options, trade-offs, risks, costs, and expected value to stakeholders.
• Communicate sophisticated AI and data concepts clearly to non-technical audiences.
• Build consensus across business, technology, architecture, security, and governance stakeholders.
• Guide proposed solutions through governance and formal approval processes.
• Serve as a trusted technical advisor to business leaders, engineering teams, and other stakeholders.
• Demonstrate the value and feasibility of proposed solutions through prototypes and clear technical recommendations.

Implementation & Production Delivery

• Work closely with engineering, data science, platform, architecture, and product teams to move approved solutions into production.
• Provide hands-on technical leadership throughout implementation.
• Guide teams on architecture, development patterns, data modeling, integration, and AI implementation.
• Ensure solutions remain aligned with approved requirements and architecture throughout delivery.
• Support troubleshooting and resolution of complex architecture and implementation challenges.
• Demonstrate experience taking solutions from initial concept through approval, implementation, and production deployment.

Governance, Security & Responsible AI

• Ensure data and AI architectures comply with enterprise security, privacy, governance, and regulatory requirements.
• Incorporate data governance principles into architecture and implementation decisions.
• Support appropriate controls around sensitive enterprise data and AI solutions.
• Incorporate responsible AI practices into solution design and delivery.
• Consider performance, maintainability, cost efficiency, security, and governance when selecting technologies and architecture patterns.
• Support MLOps and operational practices for production AI solutions.

Technical Leadership & Mentorship

• Provide architecture guidance to engineers, analysts, data scientists, and other technical team members.
• Mentor teams on modern data architecture, cloud, AI/ML, and generative AI practices.
• Champion reusable architecture patterns and strong engineering practices across the organization.
• Help establish standards and best practices for enterprise data and AI development.

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 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.
• Broad database experience spanning relational, NoSQL, analytical/data warehouse, and vector and/or graph technologies.
• Practical experience across multiple AI/ML and generative AI technologies, including areas such as 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.
• Excellent written, verbal, presentation, and stakeholder communication skills.

Critical Skills

Skill Area Target Experience Priority Information Architecture 6+ years Critical Data Analysis 6+ years Critical Complex Problem Solving 6+ years Critical Qualitative Data Analysis & Synthesis 6+ years Critical Data Modeling & Prototyping 4-6+ years Critical Data Environment Design 4-6+ years Critical Artificial Intelligence 4-6+ years Critical Multi-Cloud Architecture Demonstrated experience Critical Agentic / AI-Assisted Development Demonstrated experience Critical Requirements & Stakeholder Engagement Demonstrated experience Critical

Preferred Qualifications

• Strong and recent Microsoft Azure architecture experience.
• Experience with Azure data services, Microsoft Fabric and/or Azure Synapse.
• Hands-on experience with Azure OpenAI and Azure Machine Learning.
• Development experience with Microsoft Copilot technologies, including GitHub Copilot, Copilot Studio, and/or Microsoft 365 Copilot.
• Experience with enterprise RAG and generative AI architectures.
• Experience with MLOps and production AI lifecycle management.
• Familiarity with enterprise data governance and responsible AI practices.
• Experience supporting large, complex enterprise environments.
• Experience within public sector, transportation, transit, or infrastructure organizations.
• Relevant cloud, AI, data, or architecture certifications.

Technologies & Areas of Expertise

• Microsoft Azure
• Microsoft Fabric
• Azure Synapse
• Azure OpenAI
• Azure Machine Learning
• AWS / GCP
• Generative AI / LLMs
• Retrieval-Augmented Generation (RAG)
• Agentic AI / AI-Assisted Development
• GitHub Copilot
• Copilot Studio
• Microsoft 365 Copilot
• Machine Learning
• MLOps
• Data Engineering
• Data Architecture
• Data Modeling
• Relational Databases
• NoSQL Databases
• Data Warehouses / Analytical Platforms
• Vector Databases
• Graph Databases
• Enterprise Data Governance
• Responsible AI