Senior Solutions Architect

Position Summary

The AI Solutions Architect owns the architecture and integration map for every AI initiative across the enterprise. Serving as the technical leader and strategic advisor within the AI Center of Excellence (AI CoE), this individual oversees all AI projects from ideation through production deployment, ensuring best practices, consistent design patterns, and sound governance are applied across the portfolio.

The role maintains a comprehensive map of every AI initiative, documents how AI systems connect to enterprise platforms such as Salesforce, ERP, and ServiceNow, and designs scalable solutions that align with business objectives. The AI Solutions Architect acts as the connective tissue between business stakeholders, engineering teams, and vendor partners, translating complex requirements into actionable architectures and presenting clear trade-offs to senior leadership.

Is this the right role for you?

This is not a data architecture role. This role owns the architecture, standards, and integration map across the enterprise AI portfolio. It does not own data models, pipelines, lakehouse design, or data lineage.

If your background centers on data modeling, ETL/ELT, Databricks, or Snowflake architecture, please see our companion posting, Senior AI Data Solutions Architect.

Key Responsibilities

AI Program Oversight & Governance

• Oversee all AI projects and initiatives across the organization, ensuring alignment with the AI CoE’s strategic roadmap and enterprise architecture standards.
• Establish and enforce best practices, design patterns, and governance frameworks for AI solution development, deployment, and lifecycle management.
• Conduct architecture reviews for all new AI initiatives to validate technical approach, scalability, security, and compliance requirements.
• Define and maintain AI solution architecture standards, including model serving, API design, integration patterns, and observability.
• Define and design AI skill files that follow the standards set.

Documentation & Initiative Tracking

• Create and maintain a comprehensive catalog of all AI projects, including status, ownership, technology stack, data sources, and business impact metrics.
• Develop and manage initiative mapping documentation that clearly illustrates relationships, dependencies, and shared components across AI projects.
• Produce Technical Requirements Documents (TRDs), architecture diagrams, and design specifications for each initiative.
• Maintain a living knowledge base that captures lessons learned, reusable patterns, and reference architectures for the AI CoE.

Stakeholder Engagement & Solution Design

• Meet regularly with business stakeholders, product owners, and department leaders to gather requirements, understand pain points, and identify high-value AI opportunities.
• Translate business requirements into detailed technical solution designs, including system architecture, integration specifications, and data flow diagrams.
• Present solution proposals and architecture recommendations to senior leadership, clearly articulating trade-offs, timelines, and resource needs.
• Facilitate cross-functional design sessions and workshops to align technical and business teams on AI initiative scope and approach.

Integration Architecture & Connectivity

• Own the master integration map across all AI projects, documenting every touchpoint between AI systems, enterprise platforms (Salesforce, ServiceNow, ERP, Snowflake, etc.), and external services.
• Design and standardize integration patterns, including API contracts, event-driven architectures, synchronization strategies, and middleware configurations.
• Identify opportunities for shared services, reusable components, and platform consolidation to reduce duplication and accelerate delivery.
• Collaborate with infrastructure, platform, and data teams to ensure integration points are secure, performant, and well-monitored.

Technology Strategy & Innovation

• Evaluate emerging AI/ML technologies, platforms, and frameworks (e.g., Gemini, Vertex AI, LangChain, vector databases) and provide recommendations for adoption.
• Partner with the Agent Development team to ensure AI agents follow consistent architectural patterns and integration standards.
• Contribute to vendor evaluation and selection processes, including reviewing SOWs, conducting technical due diligence, and validating proof-of-concept results.
• Stay current on AI industry trends, security considerations, and regulatory developments relevant to AI deployments in retail and automotive sectors.

Required Qualifications

• 8+ years of experience in enterprise architecture, solutions architecture, or a senior technical role with a focus on AI/ML systems.
• Proven track record of overseeing and architecting multiple AI/ML initiatives across a complex, multi-platform enterprise environment, not a single product or project.
• Hands-on experience designing integrations between AI solutions and enterprise business platforms such as Salesforce, ERP, and ServiceNow.
• Deep understanding of AI/ML concepts, including large language models, agentic AI architectures and multi-agent orchestration, natural language processing, and computer vision.
• Strong experience with modern API and integration design (REST, GraphQL, event-driven), enterprise integration platforms, and middleware.
• Strong experience with at least one major cloud platform (GCP, AWS, or Azure).
• Demonstrated ability to create clear, comprehensive technical documentation, including architecture diagrams, TRDs, and integration specifications.
• Excellent communication and stakeholder management skills, with the ability to translate between technical and business audiences and present to senior leadership.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field; Master’s degree preferred.

Preferred Qualifications

• Hands-on experience with Google Cloud Vertex AI, Gemini, or similar enterprise AI platforms.
• Experience integrating AI solutions with enterprise data platforms such as Snowflake or BigQuery.
• Experience establishing or operating within an AI Center of Excellence or AI governance function.
• Experience with CI/CD pipelines (e.g., Harness), observability platforms (e.g., Dynatrace), and infrastructure-as-code practices.
• Knowledge of AI security, prompt injection mitigation, and responsible AI governance.
• Professional certifications in cloud architecture (GCP Professional Cloud Architect, AWS Solutions Architect) or AI/ML specializations.