Revenue Systems Engineer - FrankieOne

Role Purpose

Design, build, and own the production systems that power FrankieOne's revenue operations. The Revenue Systems Engineer is a senior technical role responsible for end-to-end ownership of data pipelines, AI/ML platforms, automation infrastructure, and internal tooling that enable the RevOps function to operate at scale. This is not a support or maintenance role — it is an engineering ownership role with direct business impact.

You will work within the Leverage Team, partnering closely with the Revenue Systems Engineer and the RevOps Manager to identify high-leverage problems and translate them into reliable, measurable production systems embedded in daily workflows across operations, management, sales, and client teams.

Key Responsibilities

AI & Machine Learning Systems (~35%)

Design, build, and maintain end-to-end ML systems including training pipelines, model serving, and API deployment for revenue-impacting use cases (scoring, classification, prediction).

Develop and operate AI-powered content generation and analysis systems that produce production-ready output at scale.

Build evaluation pipelines, feedback loops, and regression monitoring frameworks to ensure ongoing model performance.

Own the full lifecycle of deployed AI systems, including architecture, deployment, performance monitoring, and continuous improvement.

Identify high-value automation opportunities across the revenue workflow and design AI-first solutions to address them.

Data Platform & Backend Engineering (~30%)

Design and operate high-throughput data pipelines processing millions of records per day across both batch and real-time modes.

Build and maintain backend APIs and processing services that integrate HubSpot, Xero, Redshift, and other revenue-critical systems.

Architect scalable, low-latency data infrastructure that supports operational, analytical, and reporting needs.

Develop commission calculation engines, financial reconciliation systems, and contract data extraction pipelines.

Own data quality monitoring, alerting, and incident response for production systems.

Internal Platforms & Tooling (~20%)

Build internal tools and dashboards (React, Python) that are adopted and used daily across sales, operations, and management teams.

Develop email processing, workflow classification, and automation APIs that remove manual work from operational processes.

Create reporting and analytics services for enterprise clients and account managers.

Build training data systems and evaluation infrastructure that enable the team to develop and iterate on AI capabilities.

Maintain and improve the existing portfolio of production RevOps tools with a focus on reliability and performance.

Engineering Standards & Collaboration (~15%)