AI First Enterprise Architecture & Strategy

• Define and own JSSI’s AI First enterprise architecture vision, aligning agentic engineering, product platforms, data strategy, and modernization with business priorities across maintenance, parts, leasing, programs, asset management, and customer operations.

• Architect enterprise-scale AI ecosystems spanning multi-agent systems, retrieval-augmented generation (RAG) and knowledge platforms, MCP-enabled tooling, model lifecycle management, and AI-assisted SDLC capabilities.

• Establish the Microsoft/Azure-first architecture posture (C#/.NET, ASP.NET Core, React/TypeScript, Azure SQL, Service Bus, Entra ID, Key Vault, App Services, AKS/ACR, Application Insights, and secure CI/CD) as the default for new and modernized systems.

• Operationalize Claude Code Enterprise as a strategic accelerator across specification, code generation, refactoring, testing, documentation, and review, supporting JSSI’s goal of 100% AI-generated code with human accountability for intent, architecture, and quality.

Architecture Review Board, Standards & Governance

• Chair the Architecture Review Board (ARB), setting the agenda, cadence, and decision rights that drive consistent architecture across engineering verticals.

• Advocate and uphold shared standards across the AI Solution Architects embedded in each vertical, ensuring teams build on common patterns, contracts, and guardrails rather than divergent implementations.

• Define and maintain API design, event-driven, data contract, versioning, and interoperability standards, along with the agent behavior boundaries and evaluation frameworks that keep AI deployment responsible and predictable.

• Establish governance for AI First development: code ownership, review expectations, IP and secrets protection, auditability, approved model usage, and human-in-the-loop accountability, in partnership with Security, Legal, and Infrastructure.

• Ensure responsible AI across everything that ships: fairness, explainability, model monitoring, security posture, and regulatory alignment.

Reference Architectures & Platform Alignment

• Publish and mature reference architectures, architecture decision records (ADRs), reusable patterns, and engineering playbooks that improve delivery speed without sacrificing quality.

• Standardize how teams build and consume MCP servers that expose JSSI enterprise systems (Salesforce, Dynamics 365 F&O, Microsoft Fabric, and proprietary products) as secure, model-ready tools.

• Ensure AI systems and automation integrate reliably with JSSI’s enterprise platforms, coordinating with the data and platform teams that own them.

• Define observability, lifecycle-management, performance, and resiliency standards that keep production AI systems dependable as they scale.

• Drive measurable architecture maturity through ADR discipline, pattern reuse, API consistency, reduced rework, and adoption metrics.

Project Intake, Discovery & Emerging Capability Adoption

• Serve as the architectural intake gate for new initiatives, assessing incoming project and product requests for architectural fit, AI suitability, feasibility, build-versus-buy options, risk, and alignment to enterprise standards before they enter delivery.

• Provide clear intake recommendations and sizing inputs to the SVP, Product, and engineering leaders so the organization commits to the right work with full visibility into architectural impact.

• Maintain a forward view of the AI and agentic landscape (models, tools, frameworks, and vendor capabilities) so JSSI knows what is coming and can plan deliberately rather than react.

• Run structured discovery, proofs of concept, and capability spikes that validate promising technologies against JSSI’s Microsoft/Azure stack, security posture, and business needs.

• Define the pathway for accepting proven capabilities into production: how new tools and patterns are vetted, standardized, folded into reference architectures, and adopted across teams.

Leadership, Mentorship & Stakeholder Partnership

• Directly lead and develop two AI Solution Architects, owning their direction, coaching, and growth as the enterprise architecture team.

• Guide and influence the AI Solution Architects and engineering leaders across verticals through the ARB and technical leadership rather than direct management.

• Stay close enough to the work to prototype, review designs, challenge assumptions, and demonstrate credible hands-on engineering judgment, setting the standard the organization is expected to meet.

• Build trusted relationships across Business Units, Engineering, Product, Architecture, Security, Infrastructure, Data, and Operations, translating business needs into sound architectural direction and a prioritized pattern roadmap.

• Translate architecture strategy, risks, adoption, and business impact into clear executive-level narratives and operating reviews.

Core Experience

• 12+ years in software engineering and architecture, including 5+ years in a solution architect, principal engineer, or enterprise/product architecture leadership role with ownership of system design for production SaaS platforms.

• Demonstrated experience defining architecture strategy for cloud, distributed systems, APIs, integrations, and data platforms at enterprise scale.

• Production-level fluency in the Microsoft stack (C#/.NET, ASP.NET Core, REST APIs, React/TypeScript, SQL Server / Azure SQL) and distributed-systems patterns such as queues, caching, and scalable APIs.

• Hands-on experience designing and implementing AI First, spec-driven (SDD) workflows and agentic systems across the software delivery lifecycle.

• Track record establishing architecture standards, review processes, and governance that improved consistency, security, and delivery speed across multiple teams.

• Proven ability to influence executives and cross-functional leaders, and to mentor senior architects and engineers through influence rather than direct people management.

• Excellent written and verbal communication skills; able to translate complex technical concepts for technical and non-technical audiences alike.

AI & Claude Ecosystem Proficiency (Claude Strongly Preferred)

• Hands-on experience with AI coding agents and agentic workflows (Claude Code strongly preferred; GitHub Copilot, Codex, or equivalent), including CLI integration and multi-agent pipelines.

• Hands-on experience with multi-agent design patterns (planning, orchestration, observability) and MCP server implementation against enterprise data sources.

• Production expertise with LLM APIs (Claude API preferred): tool use, structured outputs, document processing, streaming, and rate-limit management.

• Strong prompt engineering skills, including structured outputs and retrieval-augmented prompting.

• Working knowledge of AI evaluation frameworks (quality, cost, latency) and responsible-AI design aligned with Anthropic’s principles.

Highly Desired Qualifications

• Experience in aviation, transportation, asset management, maintenance, aftermarket services, or other complex operational B2B domains.

• Experience with Azure AI Foundry, Azure OpenAI, Microsoft Fabric/OneLake, Semantic Kernel, Power Platform, or comparable AI engineering ecosystems.

• Experience modernizing legacy platforms through incremental architecture, API-led integration, event-driven patterns, cloud migration, and test automation.

• Familiarity with DORA metrics, SPACE concepts, and developer productivity telemetry.

• Integrations with Dynamics 365 F&O, Salesforce or equivalent CRM, and Microsoft Fabric.

• Experience in regulated, safety-sensitive, or audit-heavy environments where security, governance, and operational reliability matter.

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent professional experience; advanced degree a plus.