AI Solutions Architect

Responsibilities & Deliverables

• Define reference architectures and reusable patterns for AI use cases across functions (Sales, Services, Customer Support, Finance, HR etc.).
• Set integration, security and guardrail standards together with tech ops.
• Build telemetry and observability into AI tools to measure adoption and outcomes.
• Assess vendors and their tools for technical fit and build-versus-buy.
• Align with the Product AI team on the shared stack and MCP.
• Run technology watch on what is worth adopting.

Required Skills & Experience

• 5+ years in solution or technical architecture, with hands-on AI, ML or LLM systems.
• A track record shipping or integrating production AI tooling: APIs, agents, retrieval systems and MCP-style integrations.
• The ability to set reuse and quality standards that multiple teams actually adopt.
• Credibility with engineers and business stakeholders in the same room.
• Familiarity with enterprise AI stacks and application AI features (Salesforce, Microsoft, AWS Bedrock, Anthropic), plus AI telemetry and observability.
• Bonus: fintech or enterprise SaaS; data-platform or cloud-architecture depth.

What Success Looks Like

• A published set of reference architectures and reusable patterns that teams build against by default.
• Integration, security and guardrail standards are agreed with tech ops and consistently applied.
• New use cases reuse the shared stack rather than creating one-offs — delivery is faster and cheaper.
• Vendor and build-versus-buy assessments are trusted and repeatable.
• Adoption and outcomes are visible through telemetry, informing the next architecture decisions.