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Global66 is building a global financial services ecosystem across Chile, Peru, Colombia, and Argentina. The AI Growth Engineer role combines commercial acumen with technical AI expertise to identify and solve uncaptured opportunities in B2B sales cycles using autonomous AI agents.
Key Responsibilities:
Build and operate autonomous commercial agents: Design, launch, and operate multi-channel AI agents (email, WhatsApp, voice calls, etc.) from initial design through continuous optimization. Maintain multiple agents and workflows running in parallel on Python and cloud infrastructure, owning stability, cost, and daily behavior.
Execute B2B acquisition strategy: Execute the defined B2B acquisition strategy across segments and ICPs—build campaigns by segment, decide channels and contact cadence, construct lead databases, and maintain weekly contact volume. Identify where automation or AI agents add the most value in the sales cycle and bring those solutions to production.
Design and integrate workflows: Combine AI agents with business logic and integrate with internal systems (CRM, databases, partner tools, other team processes). Maintain end-to-end ownership of what you build.
Collaborate across teams: Gather feedback from executives and team leads to identify high-value optimizations. Train the commercial team on agents and systems built. Collaborate with Growth, Product, Onboarding, CX, Data, and Operations on strategic prioritization.
Measure, analyze, and experiment: Monitor daily commercial cycle health and review the funnel weekly by stage, channel, and segment. Run controlled A/B tests on prompts, scripts, contact timing, and prioritization criteria. Adjust agents based on evidence to optimize conversion, precision, and cost.
Requirements:
Education: Engineering, Data Science, Administration, Economics, Mathematics, Statistics, or related fields. Specialization in AI, Machine Learning, or Data Engineering is desirable.
Experience: Demonstrated experience in B2B sales cycles; proven hands-on experience with AI; knowledge of AI agent architecture and automation tools (n8n, Make, Zapier, LangChain, CrewAI, or similar).
Technical skills: Python and SQL applied to daily role tasks—maintaining agents in production on cloud platforms (AWS, Azure, etc.), reading external code, fixing failed workflows, deploying changes, and independently analyzing commercial databases. Knowledge of generative AI tools and advanced prompt engineering.
Culture fit: High-performance team member with hunger for growth and passion for challenging the financial status quo.
