AI Architect - Remote

CentralSquare Technologies•,

2d•Remote

About The Position

This is an individual-contributor architecture role, and a deliberately hands-on one. You will spend most of your time designing and building — prototyping, writing production code and infrastructure, and working directly in the codebases involved. You will also spend meaningful time with product teams across the company, each with its own architects, roadmap, and deep domain expertise, where your influence will come from collaboration and demonstrated results rather than from reporting lines. Expect variety. A given week may include architecture and design work, implementation, embedding with a product team to resolve a dependency, and presenting ideas to internal stakeholders and to customer design partners.

Requirements

Individual-contributor architecture role
Hands-on design and building
Prototyping
Writing production code and infrastructure
Working directly in codebases
Collaboration with product teams
Demonstrated results
Architecture and design work
Implementation
Embedding with product teams to resolve dependencies
Presenting ideas to internal stakeholders and customer design partners
Design and build agent systems (core reasoning loops, planning, state and memory management, tool interfaces, sub-agent delegation, orchestration for multi-turn and autonomous agents)
Make agent output trustworthy (confidence scoring, routing, human-in-the-loop design, audit trails, explainability)
Evaluation practice (golden datasets, regression gates, staged/canary rollouts, model comparison, drift monitoring)
Adversarial and failure-mode design (prompt injection, jailbreak threat modeling, containment, deterministic controls, capability decisions)
Agent-level observability (model calls, tools, inputs, decision basis)
Make existing portfolio accessible to agents (build MCP servers, APIs, wrappers, adapters)
Contribute to data pipelines and ingestion layers
Instrument existing applications
Partner with product teams (architects, engineering leaders)
Design solutions with teams and support them
Engage with customers (design partner agencies, end users)
Model current AI-assisted engineering practice (skills, commands, hooks, MCP servers, sub-agents, specification-driven workflow, deterministic quality gates)
Proficiency in .NET and Python services

Responsibilities

Design and build agent systems. Core reasoning loops, planning, state and memory management, tool interfaces, sub-agent delegation, and orchestration for multi-turn and autonomous agents.
Make agent output trustworthy enough to act on. This is central to the role rather than a final compliance step: Confidence scoring and routing — determining what can proceed automatically, what warrants a brief human review, and what requires a person to handle directly; Human-in-the-loop design that keeps professional judgment, and accountability, with the practitioner; Audit trails and explainability sufficient to withstand evidentiary and public-records scrutiny; Evaluation practice: golden datasets, regression gates on model and prompt changes, staged and canary rollout, comparison of candidate models against those in production, and monitoring for drift after release; Adversarial and failure-mode design: prompt injection and jailbreak threat modeling, containment of the effects of an incorrect action, deterministic controls around probabilistic components, and clear decisions about capabilities we choose not to build; Agent-level observability from the outset — which model call, which tool, which inputs, and the basis for the agent's decision.
Make the existing portfolio accessible to agents. Our products span a wide range of technologies, from modern .NET and Python services to long-lived platforms that predate current frameworks. You will build MCP servers, APIs, wrappers, and adapters that expose product capability to agents; contribute to the data pipelines and ingestion layers that feed them; and help instrument existing applications to understand how they are used today.
Partner across product teams. You will work alongside architects and engineering leaders who know their products deeply. Depending on the situation, that may mean designing a solution with a team and supporting them as they build it, or coming up to speed on their stack and delivering the work yourself — then walking the team through the approach so they can carry it forward.
Engage with customers. Work with our design partner agencies and end users to ground the work in real operational practice, and bring what you learn back into the design.
Model current AI-assisted engineering practice. You will bring a well-developed harness of your own: skills and commands, hooks, MCP servers, sub-agents, specification-driven workflow, and deterministic quality gates — compilers, test suites, static analysis, custom lint rules, and architecture-conformance checks — so that non-deterministic tooling produces predictable, standards-conforming output. Teams you work with will learn a great deal simply from how you work.