Head of Engineering, GTM Systems (AI Systems)
H
HuzzleSaaS, MarTech, B2B services
Argentina. Canada. United Kingdom. Poland. Estonia. RomaniaFull-TimeLead
Salary not disclosed
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Job Details
- Required Skills
- PythonArtificial IntelligenceJavascriptTypeScriptCI/CDDevOpsLLMCRM
Requirements
- Proven experience building GTM automation systems in production (e.g., enrichment workflows, API integrations, CRM automation)
- Strong understanding of data pipelines, webhooks, and multi-step automation logic
- Hands-on coding experience in Python, JavaScript, or TypeScript
- Experience working with AI/LLM-powered systems (prompt design, chaining, context handling)
- Deep understanding of outbound sales workflows and revenue operations
- Experience leading or mentoring engineers or technical teams
- Production-level engineering experience with scalable systems
- Background in agencies, consultancies, or multi-client environments
- Familiarity with DevOps practices (CI/CD, monitoring, cloud infrastructure)
- Active participation in automation, AI, or GTM engineering communities
Responsibilities
- Lead the transition from no-code automation to AI-agent-driven, code-first infrastructure
- Own the architecture powering revenue operations
- Build intelligent agent systems
- Lead a team of engineers delivering high-impact GTM infrastructure across multiple client environments
- Design and build AI-powered GTM systems—from signal detection and enrichment to campaign orchestration and response handling
- Develop scalable backend systems using Python, JavaScript, or TypeScript
- Architect integrations across CRMs, APIs, data sources, and outreach platforms
- Build reusable agent frameworks, templates, and SDK-like components for rapid deployment
- Lead and scale a distributed team of engineers
- Establish best practices for version control, testing, documentation, and deployment
- Mentor automation-focused engineers into AI-agent and software engineering roles
- Own hiring, onboarding, and performance management
- Drive the shift from no-code workflows to code-first AI systems
- Evaluate emerging AI tools, LLM capabilities, and automation frameworks
- Contribute to internal AI tooling and reusable automation libraries
- Prototype and validate new automation opportunities quickly
- Ensure system reliability through monitoring, logging, and error handling
- Optimize infrastructure for cost, performance, and scalability
- Build observability systems to proactively detect and resolve issues
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