AI Architect - GTM Systems

About This RoleAI processing…

Key Responsibilities

• 1
  Define and own the technical vision for AI-powered GTM systems — establishing the architecture strategy that covers both low-code automation (Workato, MuleSoft, Salesforce Flow) and custom agentic AI builds (LangGraph, LangChain, MCP-enabled pipelines)
• 2
  Create and publish the reference architectures, design patterns, and decision frameworks that guide when and how the GTM engineering team builds: low-code-first vs. custom, synchronous vs. event-driven, human-in-the-loop vs. fully automated
• 3
  Lead architectural design for the highest-complexity GTM systems — multi-system integrations, enterprise-scale agentic workflows, real-time event processing, and cross-platform data orchestration across Salesforce, NetSuite, Marketo, and custom data stores
• 4
  Drive the evaluation, selection, and adoption of emerging AI tools, frameworks, and platforms — assessing fit, risk, and maturity to ensure Samsara stays at the leading edge without accumulating technical debt
• 5
  Serve as the organization's technical authority on LLM orchestration: prompt safety, retrieval-augmented generation, model context management, tool calling, multi-agent coordination, and responsible AI design
• 6
  Own the integration architecture strategy across the GTM systems landscape — defining the patterns, APIs, event contracts, and data models that allow GTM systems to evolve independently while staying reliably connected
• 7
  Champion a scalable low-code-first strategy: define the governance model, extensibility patterns, and operational standards that allow non-engineers to build automations safely without creating fragility or compliance risk
• 8
  Partner with VP-level GTM stakeholders to translate strategic business goals into concrete architectural roadmaps — with clear trade-offs, timelines, and success criteria
• 9
  Conduct architecture reviews across the GTM engineering team: provide structured feedback, identify systemic risks, and drive continuous improvement in system design quality
• 10
  Produce internal thought leadership — white papers, architecture decision records (ADRs), and engineering wikis — that raise the collective technical bar across the GTM systems organization

Requirements

• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience
• 10+ years of software engineering experience, with a track record of building and shipping production-grade backend systems
• Minimum of 3 years of Technical Lead experience
• Demonstrated experience personally building and deploying AI agents used by real end users — you can walk through one specific agent: the persona, the problem, the build, and the outcome
• Hands-on proficiency with LLM orchestration and agentic frameworks: LangGraph, LangChain, CrewAI, LlamaIndex, or equivalent
• Experience designing and implementing RAG systems: vector databases, retrieval strategies, chunking approaches, and embedding pipelines
• Strong backend engineering fundamentals: Python and/or Node.js, FastAPI or equivalent, async and event-driven architecture patterns
• AWS experience: Bedrock, Lambda/serverless, SQS/SNS/EventBridge, or equivalent cloud-native infrastructure
• Strong communication skills and the ability to work directly with non-technical business stakeholders to extract requirements and translate them into buildable solutions
• Experience building evals and observability tooling for LLM-powered systems: LangFuse, tracing, benchmark suites, or custom harnesses
• Familiarity with sales, deal desk, or finance workflows — enough to understand how a deal moves from lead to quote to close to renewal and where the friction lives
• Experience with FastMCP, LiteLLM, or Model Context Protocol (MCP) in production multi-agent systems
• Exposure to Salesforce as a data source — not as an admin, just enough to know where relevant GTM data lives and how to access it
• Background at a startup building AI for GTM or RevOps, or a RevOps background where you pivoted into AI engineering
• Experience mentoring engineers or contributing to hiring, even informally
• Familiarity with prompt engineering, tuning, and understanding of how model behavior changes across LLM providers (OpenAI, Anthropic/Claude, etc.)
• Experience with full-stack development: custom application development, AWS Bedrock, Node.js, Python, serverless programming

Perks & Benefits