AI Architect - GTM Systems

Role overview Lead AI Architect for go-to-market (GTM) systems, responsible for shaping the technical vision behind AI-powered automation across a large enterprise stack. This is a senior individual contributor role that combines architecture, hands-on prototyping, and cross-organizational influence — spanning both low-code platforms and custom-built agentic systems.

Responsibilities - Define and own the technical vision for AI-powered GTM systems, covering low-code automation (Workato, MuleSoft, Salesforce Flow) and custom agentic builds using frameworks like LangGraph, LangChain, and MCP-enabled pipelines. - Create reference architectures, design patterns, and decision frameworks that guide when teams should reach for low-code tools versus custom code, synchronous versus event-driven flows, and human-in-the-loop versus fully automated solutions. - Lead architectural design for high-complexity GTM integrations across Salesforce, NetSuite, Marketo, and custom data stores, including real-time event processing and cross-platform orchestration. - Evaluate and drive adoption of emerging AI tools and frameworks, assessing fit, risk, and maturity without accruing technical debt. - Serve as the organization's technical authority on LLM orchestration topics: prompt safety, RAG systems, model context management, tool calling, and responsible AI design.

Requirements - 10+ years of software engineering experience shipping production-grade backend systems, with at least 3 years in a Technical Lead capacity. - Demonstrated experience personally building and deploying AI agents used by real end users. - Hands-on proficiency with LLM orchestration frameworks such as LangGraph, LangChain, CrewAI, or LlamaIndex. - Experience designing RAG systems, including vector databases, retrieval strategies, and embedding pipelines. - Strong backend fundamentals in Python or Node.js, with experience in async and event-driven architectures. - AWS experience (Bedrock, Lambda/serverless, SQS/SNS/EventBridge) or equivalent cloud-native infrastructure, plus strong communication skills for working with non-technical stakeholders.

Nice to have - Familiarity with building evals and observability tooling for LLM systems (LangFuse, tracing, benchmark suites). - Exposure to sales, deal desk, or finance workflows, including how leads progress through quote-to-renewal cycles. - Experience with FastMCP, LiteLLM, or Model Context Protocol in production multi-agent systems. - Familiarity with Salesforce as a data source, prompt engineering across providers, and full-stack development including custom applications and serverless programming.