Software Architect (AI & Cloud) - Remote

At CentralSquare, we don’t just build software - we power public servants and uplift communities with Hero-Grade Technology. Every line of code, every feature we deliver helps heroes across North America protect, serve, and save lives. When you join us, you become part of a mission-driven team creating technology that makes communities safer and stronger.

• →Define and own the cloud-native architecture for C# services, APIs, and containerized workloads running on AWS EKS, including service decomposition, data access patterns, and cross-cutting concerns.
• →Lead monolith decomposition strategy — identifying bounded contexts, defining service boundaries, and creating incremental extraction plans that let teams carve out services from legacy systems without big-bang rewrites.
• →Architect the migration from legacy ASP.NET WebForms (.aspx) or other legacy UI systems to modern SPA frameworks (Angular or equivalent), defining the incremental migration path, component architecture, state management patterns, and coexistence strategy that lets teams move page by page without a full rewrite.
• →Drive OIDC modernization across products — migrating from legacy or inconsistent authentication patterns to standards-based OIDC flows
• →Architect event-driven and event-sourced systems, designing domain event contracts, message schemas, and replay/projection patterns that support offline capability, audit trails, and eventual consistency across distributed services.
• →Design and implement GraphQL APIs as a unified query layer across decomposed services, including schema federation, resolver architecture, and performance considerations like query complexity limits and dataloader patterns.
• →Design and evolve AI orchestration pipelines — multi-agent architectures, sub-agent delegation, spec-to-code-to-test workflows — that serve as the primary mechanism for code production across teams.
• →Prepare products for AI integration by designing MCP-ready architectures — exposing product capabilities as well-defined tool interfaces, structured context providers, and machine-consumable APIs that allow AI agents and third-party integrations to interact with our platform programmatically.
• →Establish the specification framework that feeds AI-driven development: what a spec must contain, how it maps to architecture context, data contracts, and behavioral validation, so that AI output is predictable and production-grade.
• →Drive legacy system refactoring strategy, making pragmatic decisions about what to rewrite, what to wrap, and what to strangle — balancing technical debt reduction against delivery velocity and risk.
• →Architect for resilience and scalability — circuit breakers, retry policies, graceful degradation, horizontal scaling patterns, backpressure handling, and health check architectures that keep systems stable under load and failure.
• →Define and enforce observability architecture — distributed tracing, structured logging, metrics, and DAU telemetry — as a first-class system concern, not an afterthought.
• →Integrate AI tooling (Claude, Copilot, custom agents) into development workflows, continuously raising the ceiling on what automated pipelines can reliably deliver.