AI Architect - GTM Systems

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.

• Define and own the technical vision for AI-powered GTM systems, covering low-code automation (Workato, Mule Soft, Salesforce Flow) and custom agentic builds using frameworks like Lang Graph, Lang Chain, 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, Net Suite, 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.

• 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 Lang Graph, Lang Chain, CrewAI, or Llama Index.
• 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/Event Bridge) or equivalent cloud-native infrastructure, plus strong communication skills for working with non-technical stakeholders.

• Familiarity with building evals and observability tooling for LLM systems (Lang Fuse, 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.