AI Architect - GTM Systems

Role overview

Lead the technical direction for intelligent, automated go-to-market systems in a senior individual-contributor architecture role. The work spans low-code automation, custom AI agents, enterprise integrations, and governed production platforms, translating business priorities into systems that improve workflows across sales and revenue operations. This is a remote position open to candidates in Canada.

Responsibilities

- Define reference architectures and technical standards for AI-powered go-to-market automation. - Decide when to use configurable automation platforms versus custom agentic services and integrations. - Architect, prototype, and help deliver production-grade AI workflows rather than serving only as an advisor. - Partner with engineering leaders and senior business stakeholders to create roadmaps, clarify trade-offs, and establish measurable outcomes. - Lead architecture reviews, identify systemic risks, and improve design quality across the engineering organization. - Document technical decisions through architecture records, internal guidance, and engineering knowledge bases.

Requirements

- Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience. - At least 10 years of software engineering experience building and shipping production backend systems. - At least 3 years in a technical leadership capacity. - Demonstrated experience personally building and deploying AI agents used by real end users. - Hands-on knowledge of LLM orchestration, agentic frameworks, retrieval-augmented generation, vector databases, retrieval strategies, chunking, and embedding pipelines. - Strong backend development skills with Python and/or Node.js, API frameworks, asynchronous processing, event-driven design, and cloud-native infrastructure.

Nice to have

- Experience with evaluation and observability systems for language-model applications. - Familiarity with sales, deal-desk, finance, or revenue-operations workflows. - Production experience with multi-agent protocols, model gateways, or tool-connected AI systems. - Exposure to CRM data models, prompt engineering, model-provider differences, mentoring, hiring, or full-stack development.