Job Title: GenAI Ops Solution Architect
Duration : Permanent Full Time
Location : Strongsville, OH, Dallas, TX, or Pittsburgh, PA.Work Mode : 5 Days Onsite Looking for a Solution Architect will lead the design, governance, and evolution of enterprise scale Generative AI platforms and solutions. This role is responsible for defining architecture standards, platform capabilities, integration patterns, governance controls, and engineering practices that enable secure, scalable, and reusable GenAI adoption across the enterprise.
The architect will work closely with business stakeholders, engineering teams, platform teams, governance organizations, and cloud providers to establish a centralized GenAIOps capability supporting Retrieval Augmented Generation (RAG), Agentic AI, ModelOps, Evaluation, Observability, and AI Governance.
Future duties and responsibilities Enterprise GenAI Architecture - Define and govern the enterprise GenAI platform architecture.
- Establish architecture standards, design patterns, and reusable frameworks for enterprise AI adoption.
- Lead solution design for RAG, Document Intelligence, Agentic AI, Evaluation,
- Observability, and Governance capabilities.
- Define reference architectures and integration patterns for onboarding GenAI use cases.
GenAIOps Platform Leadership - Drive the design and implementation of centralized GenAIOps capabilities including:
o RAG & Retrieval Services
o AgentOps
o ModelOps / LLMOps
o Evaluation Pipelines
o Observability & Monitoring
o AI Governance & Controls - Establish reusable engineering patterns and shared platform services.
Architecture Governance - Lead architecture reviews and technical governance processes.
- Ensure alignment with enterprise security, compliance, risk, and regulatory requirements.
- Define standards for responsible AI, auditability, traceability, and human in the loop controls.
- Participate in governance forums and stakeholder reviews.
Cloud & Integration Strategy - Define cloud architecture and deployment strategies across Azure, AWS, or hybrid environments.
- Establish enterprise integration patterns for APIs, data platforms, document repositories, workflow systems, and identity providers.
- Lead architecture decisions around scalability, resiliency, security, and performance.
Engineering Leadership - Provide technical leadership to Value Engineers, Context Engineers, Alignment Engineers, and ModelOps teams.
- Support platform onboarding and use case architecture activities.
- Mentor engineering teams and drive adoption of best practices.
- Evaluate emerging GenAI technologies and recommend platform enhancements.
Stakeholder Engagement - Collaborate with business and technology leaders to align architecture decisions with strategic objectives.
- Support roadmap planning, platform evolution, and capability expansion initiatives.
- Act as the primary architecture authority for enterprise GenAI initiatives.
Required qualifications to be successful in this role - 10+ years of experience in solution architecture, enterprise architecture, cloud architecture, or platform engineering.
- 3+ years of experience designing and implementing Generative AI and enterprise AI solutions.
- Deep understanding of:
o Large Language Models (LLMs)
o Retrieval Augmented Generation (RAG)
o Agentic AI
o Prompt Engineering
o AI Evaluation Frameworks
o ModelOps / LLMOps
o AI Governance - Experience designing enterprise scale cloud solutions on Azure, AWS, or GCP.
- Strong knowledge of microservices, APIs, event driven architectures, and distributed systems.
- Experience leading architecture governance and enterprise technology standards.
- Strong stakeholder management and executive communication skills.
- Establish a scalable and reusable enterprise GenAI platform.
- Accelerate onboarding of GenAI use cases through reusable architecture patterns.
- Ensure alignment with governance, security, and compliance requirements.
- Improve platform adoption, operational efficiency, and engineering productivity.
- Enable sustainable long term ownership through architecture standardization and knowledge transfer
Required Skills: - Agentic AI
- Generative AI
- Large Language Model (LLM)
- Retrieval-Augmented Gen.(RAG)
- Security compliancy
- Solutions Architecture
- AI Governance
- AWS Amazon Bedrock
Ref: #404-IT Pittsburgh
