Enterprise GenAI Architect

Virtual Tech Gurus; Dallas (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Virtual Tech Gurus is seeking a Senior Enterprise AI Platform Architect / Production-Readiness Engineer to guide the project from pilot exit to enterprise deployment. This embedded staff-augmentation role collaborates with Client teams to harden the AI control plane for scalable, secure production use.

The ideal candidate will bring 10+ years of enterprise architecture and 5+ years in AI/ML platforms, with a proven ability to deliver production-ready architecture in cloud/hybrid environments.

Qualifications

• 10+ years in enterprise technology architecture and related disciplines.
• 5+ years with AI/ML platforms and enterprise AI solutions.
• Proven track record taking technology platforms from pilot to production readiness.
• Embedded senior resource with strong cross-functional collaboration.
• Experience with cloud/hybrid environments and API integration.

Responsibilities

• Assess AI control-plane architecture for enterprise-scale needs.
• Evaluate scalability, resiliency, security, and operational support.
• Define patterns to support future expansion without redesign.
• Support transition from pilot to enterprise production readiness.
• Document architecture decisions and artifacts.
• Collaborate with Client teams across domains.
• Develop actionable roadmaps and integration requirements.

Skills

Enterprise architecture

AI/ML platforms

Production readiness

Cloud/hybrid environments

Stakeholder management

Communication

Embedded resource

Architectural judgment

Tools

Kubernetes

Containerization

AWS/Azure/GCP

IaC (Terraform/CloudFormation)

CI/CD

Observability tools

Security architecture

Job description

Position Overview

VTG is seeking a Senior Enterprise AI Platform Architect / Production-Readiness Engineer to work as an embedded resource. The role will operate at the critical boundary between pilot exit and enterprise production readiness, helping harden the AI control plane, resolve open scalability and architecture decisions, and establish the technical runway required for future enterprise-scale integration.

The successful candidate will work closely with Client and VTG architects, engineers, AI stakeholders, infrastructure teams, security teams, and operational stakeholders to ensure that the platform is positioned for enterprise scalability, integration, operational adoption, governance, and future expansion.

This is an embedded staff-augmentation role, operating under Client direction. The resource is not responsible for building the AI platform. The Client will continue to develop the platform in-house. The primary responsibility is to provide senior architectural and engineering expertise to help make the platform production-ready and executable at enterprise scale.

Key Responsibilities

1. Enterprise AI Platform Architecture

• Assess and validate the AI control-plane architecture against enterprise-scale requirements.
• Evaluate architecture for scalability, resiliency, availability, security, integration, and operational supportability.
• Identify architectural gaps and recommend practical approaches to address them.
• Help define architecture patterns that support future enterprise expansion without requiring significant architectural redesign.
• Evaluate technology, infrastructure, integration, and platform dependencies impacting production readiness.
• Support architecture decision-making around open scale and production-readiness questions.

2. Production Readiness & Pilot Exit

• Support the transition of the AI platform from pilot capability toward an enterprise production-ready state.
• Establish and validate production-readiness criteria and technical exit considerations.
• Identify risks, gaps, dependencies, and technical debt that could prevent successful enterprise deployment.
• Assess operational readiness, including monitoring, logging, alerting, supportability, reliability, performance, and incident response considerations.
• Provide recommendations to close identified production-readiness gaps.
• Support the Client in determining what must be addressed before moving from pilot to broader implementation.

3. Scalability & Performance

• Evaluate the AI control plane against anticipated enterprise-scale workloads and integration patterns.
• Identify current and future scalability constraints.
• Analyze capacity, performance, availability, and resiliency considerations.
• Provide recommendations for horizontal/vertical scaling, distributed architecture, workload management, and resource utilization where appropriate.
• Help resolve open scale-related architecture decisions required for future implementation.
• Ensure scalability considerations are documented and translated into actionable engineering requirements.

4. Enterprise Integration Architecture

• Define and document requirements for integrating the AI platform with existing Client enterprise platforms, infrastructure, applications, security controls, identity services, data sources, and operational systems.
• Identify upstream and downstream dependencies required for future Scale Integration.
• Evaluate APIs, interfaces, data flows, authentication/authorization mechanisms, event flows, and integration patterns.
• Develop integration recommendations and architecture artifacts that can be directly leveraged during the future implementation.
• Identify integration risks and recommend mitigation strategies.

5. Migration & Scale Integration Readiness

• Develop the technical runway required for future enterprise Scale Integration.
• Define migration considerations, sequencing, dependencies, prerequisites, and implementation risks.
• Identify what must be completed during or after the pilot to enable enterprise deployment.
• Develop practical implementation recommendations that minimize architectural recreation during the future Scale Integration phase.
• Support creation of a phased execution roadmap from pilot exit to enterprise implementation.
• Ensure architecture decisions made during the pilot can support future expansion.

6. Governance, Security & Operational Readiness

• Collaborate with Client security, infrastructure, architecture, and operations teams to validate enterprise requirements.
• Identify security, access-control, identity, data protection, compliance, and governance considerations relevant to production deployment.
• Evaluate operational ownership and support requirements for the future enterprise environment.
• Recommend appropriate monitoring, observability, logging, alerting, backup/recovery, and operational support practices.
• Document governance and operational dependencies that must be addressed for enterprise adoption.

7. Architecture Documentation & Decision Support

• Document architecture decisions, assumptions, dependencies, risks, and unresolved issues.
• Develop architecture diagrams, integration views, dependency maps, and technical decision artifacts as required.
• Maintain an actionable list of production-readiness and scale-integration considerations.
• Provide technical recommendations to Client leadership and engineering stakeholders.
• Translate complex technical issues into clear business and implementation implications.

8. Cross-Functional Collaboration

• Work directly with Client architects, engineers, AI teams, infrastructure teams, cybersecurity teams, operations, and program leadership.
• Participate in architecture reviews, technical working sessions, design discussions, and production-readiness assessments.
• Challenge assumptions constructively and provide senior-level technical guidance.
• Coordinate across teams to identify and resolve architectural dependencies.
• Operate effectively within a Client-led delivery model and follow Client technical direction, standards, and processes.

Required Qualifications

• 10+ years of experience in enterprise technology architecture, platform engineering, solution architecture, infrastructure architecture, or related disciplines.
• 5+ years of experience working with AI/ML platforms, AI infrastructure, GenAI platforms, or enterprise AI solutions.
• Demonstrated experience taking technology platforms from pilot/POC through production readiness and enterprise scale.
• Strong understanding of enterprise architecture principles and distributed platform design.
• Experience evaluating scalability, reliability, resiliency, performance, and operational readiness.
• Strong understanding of enterprise integration patterns, APIs, data flows, identity, security, and infrastructure dependencies.
• Experience working with cloud and/or hybrid enterprise environments.
• Ability to work across application, platform, infrastructure, data, security, and operations teams.
• Strong architecture documentation and technical communication skills.
• Demonstrated ability to work as an embedded senior technical resource within a customer organization.

Preferred Technical Experience

• Enterprise AI / ML platforms
• Generative AI / LLM architectures
• AI inference and model-serving platforms
• AI agents and agentic architectures
• Kubernetes / containerized platforms
• Cloud platforms such as AWS, Azure, or GCP
• Hybrid and on-premises infrastructure
• API and microservices architecture
• Enterprise integration platforms
• Data platforms and data pipelines
• Identity and access management
• Cybersecurity architecture
• Observability and monitoring
• CI/CD and DevOps
• Infrastructure as Code
• High availability and disaster recovery
• Enterprise data governance
• AI governance and responsible AI
• Platform engineering and SRE practices

Critical Behavioral & Leadership Skills

• Strong architectural judgment and ability to make pragmatic recommendations.
• Comfortable operating in ambiguous environments with evolving requirements.
• Ability to identify the right questions and decisions required to move from pilot to production.
• Strong collaboration and stakeholder-management skills.
• Ability to influence without direct authority.
• Excellent written and verbal communication.
• Ability to communicate effectively with both engineering teams and executive stakeholders.
• Strong analytical and problem-solving capabilities.
• Comfortable challenging existing designs while respecting Client ownership and direction.
• Ability to balance short‑term pilot requirements with long‑term enterprise architecture.

Expected 90-Day Outcomes

1. Strengthen the AI control plane toward production readiness.
2. Identify and close critical architecture and scalability gaps required for pilot exit.
3. Document open scale decisions and architecture decisions required for future implementation.
4. Define enterprise integration requirements and dependencies.
5. Establish migration and implementation considerations for future Scale Integration.
6. Identify operational, security, governance, and support requirements for enterprise adoption.
7. Produce an actionable technical roadmap from pilot exit to enterprise‑scale implementation.
8. Enable the Client to proceed with future Scale Integration without having to recreate or fundamentally redesign the architecture.

Role Clarification

This position is intentionally structured as an embedded senior engineering/architecture resource.

The resource is NOT expected to:

• Build the AI platform on behalf of the Client.

The resource IS expected to:

• Work alongside Client teams.
• Operate under Client/VTG direction.
• Provide senior architecture and engineering expertise.
• Help resolve production‑readiness and scalability challenges.
• Create the technical foundation and implementation runway for future enterprise Scale Integration.
• Ensure the decisions and artifacts created during the pilot can be directly leveraged during subsequent implementation.

Engagement Model

Engagement Type: Embedded Staff Augmentation

Engagement Duration: Full time

Primary Focus: Enterprise AI Platform Architecture, Production Readiness, Scalability, Integration & Migration Readiness

Reporting/Direction: Embedded within and under the direction of the Client

Role Level: Senior / Principal‑level Architect-Engineer