AI Revenue Systems Analyst

About the role

Perk is building AI infrastructure that transforms how its revenue team works. As an AI Revenue Systems Analyst you will be the technical authority on agentic AI, designing production‑grade, multi‑step AI agents that replace manual effort across the sales and GTM stack.

Key responsibilities

• Design, build, and maintain multi‑step AI agents using Python and LLM APIs (Claude, OpenAI) to automate research, qualification, routing and decision‑making.
• Develop a reusable agent framework with shared abstractions, tooling patterns and integration modules for the wider Revenue Systems and GTM Engineering teams.
• Build and operate data pipelines that connect Salesforce, outbound tools, enrichment providers and internal systems, ensuring clean, timely data for agents.
• Engineer API integrations, webhook‑driven triggers and event‑based flows using Python, Make and similar automation tools.
• Partner with GTM leadership to identify manual processes suitable for automation and translate them into scoped, shippable agent systems.
• Set and maintain engineering standards for AI systems, including version control, documentation, testing and deployment hygiene.

Required profile

• 5+ years in a technical role building production systems in a B2B SaaS environment.
• Hands‑on experience building multi‑step AI agents with Python and LLM APIs in production.
• Strong Python engineering fundamentals and ability to write clean, maintainable, testable code.
• Experience integrating APIs, webhooks and event‑driven flows using automation tooling (Make, n8n, Zapier) alongside custom Python solutions.
• Proficiency with Salesforce configuration (custom objects, flows, reporting, data integrity).
• Proven track record translating ambiguous business problems into scoped, delivered technical solutions.
• Solid system‑design instincts: observability, error handling, graceful degradation and low maintenance debt.

Required skills

• Python
• LLM APIs (Claude, OpenAI)
• Make, n8n, Zapier (automation tooling)
• API integration & webhooks
• Event‑driven architecture
• Salesforce (custom objects, flows, reporting)
• Data pipeline design and monitoring
• Observability and error‑handling patterns
• Version control, testing and deployment best practices