Lead AI Engineer- GTM Systems
$135,575–$175,450 year
On-siteAtlanta, Georgia, United States
Job Summary
Define and own the technical vision for AI-powered GTM systems, establishing architecture strategies that cover low-code automation and custom agentic builds. Create reference architectures, design patterns, and decision frameworks guiding when to use tools like Workato versus custom LangGraph agents. Lead architectural design for high-complexity integrations across Salesforce, NetSuite, and custom data stores, while driving evaluation of emerging AI tools to prevent technical debt. Serve as the organization's technical authority on LLM orchestration, prompt safety, and responsible AI design, championing a scalable low-code-first strategy with clear governance. Partner with VP-level stakeholders to translate business goals into concrete roadmaps and conduct architecture reviews to drive continuous improvement. Produce internal thought leadership including white papers and architecture decision records to raise the collective technical bar.
Required Qualifications
- 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
- 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
- This is a remote role open to candidates in Canada
Desired Qualifications
- 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
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