AI Solutions Architect

Our Client, a Financial company, is looking for an AI Solutions Architect for their Lehi, UT/Jersey City, NJ/Hybrid location.

Responsibilities:

• Business Needs and Value:
• Partner with ES service owners, Technical Operations, and BU stakeholders to understand service requirements, operational friction, and customer outcomes; assess where AI is appropriate.
• Define use cases and success criteria; assess feasibility, value, cost, risk, and reuse before recommending experiments or enhancements to existing and new ES services.
• Service Architecture and Engineering:
• Architect AI enabled capabilities within ES cloud, identity, API/MCP, integration, connectivity, and observability services; apply secure, reusable enterprise patterns.
• Lead hands-on evaluations and prototypes; guide solution development and integration through production readiness, with clear engineering acceptance criteria.
• Managed Service Lifecycle and Quality:
• Design for secure, supportable operations with service engineering and Technical Operations: evaluation, telemetry, access controls, runbooks, incident response, and rollback.
• Present evidencebased options to ES and BU leaders, explaining service value, reliability, AI quality, cost, privacy, and humanoversight tradeoffs.
• Technical Leadership and Partnerships:
• Lead cross functional architecture reviews and mentor engineers; coordinate solution delivery with BU technical teams, Security, and CIO Analytics on dependent applications and integrations.
• Translate architecture into demonstrable outcomes, communicate delivery risks, and maintain clear service and platform accountability.

Requirements:

• Bachelor's degree in computer science, information technology, engineering, or equivalent professional experience.
• 7+ years in software, platform, integration, or solutions architecture roles, with handson production engineering, technical leadership, and serviceprovider or customerfacing experience.
• Technical Expertise:
• Applied AI solution design: LLM workflows, retrieval when appropriate, tool/agent integration, quality evaluation, modelselection tradeoffs, and nonAI alternatives.
• Enterprise service integration: Cloud architecture, REST APIs, MCP, identity and authorization, operations tooling, and reusable platform and service patterns.
• Security and service reliability: Data protection, threat modeling, auditability, observability, resiliency, human oversight, and production readiness.
• Engineering and advisory: Python or TypeScript prototypes, architecture decision records, delivery tradeoffs, collaboration with service owners, and crossteam facilitation.
• Preferred Experience:
• AWS AI services, Claude, Kong Konnect AI/MCP integration, Splunk or CloudWatch, CI/CD, and engineering managed services in a regulated enterprise.

Why Should You Apply?

• Health Benefits
• Referral Program

Excellent growth and advancement opportunities