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Generative Ai Solutions Architect Jobs (NOW HIRING)

Pae

Senior AI Solutions Architect, Generative AI

The Senior AI Solutions Architect - Generative AI is the senior technical leader for the AI Center of Excellence's (AI COE) Generative AI and large language model (LLM) architecture. The role ...

CEI

AI Solutions Architect

Pittsburgh, PA · Remote

$61.25 - $80.50/hr

Design and architect scalable AI/ML solutions, including generative AI, LLM based applications, and machine learning pipelines, aligned to client business objectives. * Lead technical strategy for AI ...

HomeServe

AI Solutions Architect

Norwalk, CT · On-site

$125 - $150/hr

The AI Solutions Architect is a leadership role integrating strategic technology planning, solution ... Define scalable and secure architecture patterns for generative AI, large language models, machine ...

HomeServe

AI Solutions Architect

Norwalk, CT · On-site

$125K - $167K/yr

The AI Solutions Architect is a leadership role integrating strategic technology planning, solution ... Define scalable and secure architecture patterns for generative AI, large language models, machine ...

HomeServe USA

AI Solutions Architect

Norwalk, CT

$63.25 - $83.50/hr

The AI Solutions Architect is a leadership role integrating strategic technology planning, solution ... Define scalable and secure architecture patterns for generative AI, large language models, machine ...

VeridianTech

AI Architect

Jersey City, NJ · On-site

$65.75 - $86.75/hr

Develop and guide machine learning and Generative AI solutions across enterprise use cases. Design ... Architect and deploy AI/ML solutions within Google Cloud Platform (Google Cloud Platform ...

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How much do generative ai solutions architect jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for generative ai solutions architect in the United States is $70.17, according to ZipRecruiter salary data. Most workers in this role earn between $60.58 and $79.81 per hour, depending on experience, location, and employer.

What is a generative AI solutions architect?

A Generative AI Solutions Architect is a professional who designs, develops, and implements solutions using generative artificial intelligence technologies, such as large language models and generative adversarial networks. They work with stakeholders to understand business needs, select appropriate AI models, and ensure integration with existing systems. Their role includes overseeing the entire AI solution lifecycle, ensuring scalability, compliance, and ethical use of AI. They also stay up-to-date with advances in generative AI to recommend the best tools and practices for their organization.

What are the key skills and qualifications needed to thrive as a generative AI solutions architect?

To thrive as a Generative AI Solutions Architect, you need expertise in machine learning, deep learning, and AI model development, typically backed by a degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, cloud platforms (such as AWS, Google Cloud, or Azure), and relevant certifications (e.g., AWS Certified Machine Learning) is highly valuable. Strong problem-solving, communication, and project management skills help you translate complex AI concepts into actionable business solutions and collaborate effectively with stakeholders. These skills are vital to successfully designing, implementing, and scaling generative AI systems that meet organizational goals.

What are some common challenges faced by generative AI solutions architects when integrating AI models into existing business systems?

Generative AI Solutions Architects often encounter challenges related to aligning advanced AI models with legacy systems, ensuring data privacy and security, and managing stakeholder expectations. Integration may require custom API development, thorough testing, and close collaboration with IT and data engineering teams. Successfully navigating these complexities typically involves a combination of strong technical expertise, clear communication with non-technical stakeholders, and continuous learning to stay abreast of rapidly evolving AI technologies.

What cities are hiring for Generative Ai Solutions Architect jobs?

Cities with the most Generative Ai Solutions Architect job openings:

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Infographic showing various Generative Ai Solutions Architect job openings in the United States as of August 2026, with employment types broken down into 86% Full Time, 8% Part Time, and 6% Contract. Highlights an 78% Physical, 3% Hybrid, and 19% Remote job distribution, with an average salary of $145,963 per year, or $70.2 per hour.

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

Seneca Resources Company, LLC

New York, NY • On-site

$78 - $88.25/hr

Contractor

Posted 20 days ago

Key responsibilities

  • Design, define, and deliver enterprise-scale data and AI solutions across data ingestion, storage, transformation, modeling, and consumption.

  • Design and implement solutions leveraging generative AI, LLMs, RAG, and AI/ML frameworks, including using agentic development tooling and AI-assisted workflows.

  • Architect modern data and AI solutions with a focus on Microsoft Azure and multi-cloud environments, supporting data engineering, analytics, and AI/ML use cases.

Job description

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

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