AI Solution Architect (15 Years) (Need Local to NJ ONLY)

Lead hands-on solution architecture for Engineering Services (ES) platforms and managed business services. Partner with service owners and customers to identify where AI or simpler automation adds value, then guide design, engineering, and reliable operation of those capabilities. Specific 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 evidence-based options to ES and BU leaders, explaining service value, reliability, AI quality, cost, privacy, and human-oversight tradeoffs.

Job Description: AI Solutions Architect Purpose of the Role Lead hands-on solution architecture for Engineering Services (ES) platforms and managed business services. Partner with service owners and customers to identify where AI or simpler automation adds value, then guide design, engineering, and reliable operation of those capabilities.

Specific 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 evidence-based options to ES and BU leaders, explaining service value, reliability, AI quality, cost, privacy, and human-oversight 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.

• Bachelor’s degree in computer science, information technology, engineering, or equivalent professional experience.

• 7+ years in software, platform, integration, or solutions architecture roles, with hands-on production engineering, technical leadership, and service-provider or customer-facing experience.

Technical Expertise

• Applied AI solution design: LLM workflows, retrieval when appropriate, tool/agent integration, quality evaluation, model-selection tradeoffs, and non-AI 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 cross-team 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.