Internal Systems Engineer (Applied AI) at Impact

Your Role at impact.com:

Reporting to the Manager of Customer Operations Systems, the Internal Systems Engineer (Applied AI) is a specialized, high-impact builder role dedicated to supercharging our Customer Operations teams (Support, Success, Onboarding, Implementation).

This is a highly practical, application-focused engineering position instrumental to our AI transformation. You will spend your time across three types of work:

• Building AI-powered tools and agents. You will integrate applied AI into our workflows using modern frameworks. You will consult with business stakeholders to help explain the art of the possible, guide them in defining logic and prompts, and own building and maintaining agentic workflows and tools. Example projects include building an AI-powered QA and training tool with a RAG component for new support agents, architecting agentic workflows for our onboarding team, or building a FreshDesk integration that suggests draft responses to tickets.
• Building and maintaining a shared agent platform. This is what makes AI scale beyond our team. You will build and maintain the templates, approved patterns, and deployment infrastructure that enable people across Customer Operations to build their own agents without needing to figure out hosting, monitoring, or API connectivity themselves. When a Technical Services lead wants to build an agent that automates "pixel not firing" investigations, they should be able to do it on your platform without writing a deployment script. You own making self-serve actually work.
• Building robust internal software, integrations, and automations - such as developing custom FreshDesk widgets to support our agents. You will own the full lifecycle of these apps: building, hosting, monitoring, maintaining, and eventually retiring them when they're no longer needed.
• Many job descriptions define themselves as "critical" or "pivotal." This role genuinely is. impact.com has thousands of clients, millions of partners, endless data, and complex multi-system processes. As a business, we are ensuring we are on the cutting edge - aggressively automating and scaling up what we do with AI. This role owns a substantial portion of that transformation for a critical part of our business.
• A note on how you'll work with others: when someone across Customer Operations builds a pilot that proves value, you don't take it over. You partner with them to elevate it - bringing engineering rigor, production infrastructure, and scale while they maintain creative control and ongoing input. When someone has a great idea but isn't technical enough to build it themselves, your first instinct is to build it with them, growing their skills in the process. You're an accelerator, not a bottleneck.
• This role is for the person who embodies the overlap of curiosity, process-obsession, and action orientation. It is for the passionate tinkerer who also gets carried away exploring custom Salesforce structures and data flows. It is for the person who has been building agentic workflows to manage their household tasks in their free time because they can't stop playing with the new tools AI offers.
• If you are that person - if you are a highly motivated, thoughtful builder - and you want to leverage applied AI on a daily basis, this is the role for you.

What You'll Do:

• Software Development Foundation: Proven experience building, hosting, and maintaining applications and tools. You are comfortable writing clean, functional code (Python, JavaScript/TypeScript, etc.) to solve operational problems. You understand what it means to keep something running in production, not just get it working once.
• API & Integration Expertise: Deep experience working with REST APIs. You can easily build connectors between distinct SaaS platforms to automate data flows and trigger actions.
• Applied AI Engineering: Hands-on experience with LLM orchestration frameworks (like LangChain or LangGraph). You understand MCPs, RAG pipelines, agentic workflows, DAG vs linear flows, engineering HITL systems, and have built real-world systems using these rails.
• AI Evaluation & Quality: Experience setting up and running evals for LLM outputs. You know how to build systems that grade, evaluate, and test AI responses for accuracy and safety (e.g., LLM-as-a-judge). You understand CI/CD for AI systems and can build regression testing pipelines.
• Platform Thinking: You don't just build one-off tools - you think about how to make your work reusable. You can design templates, abstractions, and deployment patterns that other people (including non-engineers) can use to build on top of what you've created.
• Systems Thinking & Process Orientation: You don't write code in a vacuum; you map out complex operational workflows before you start building. You have a knack for seeing how different tools, teams, and data flows interact, ensuring your solutions address the actual problem without creating downstream issues.
• Cross-Functional Collaboration: Ability to work closely with non-technical business stakeholders. You can take a process flow and prompt designs from a business leader and turn them into a functioning, hosted application. Equally important: you can teach - walking a non-technical person through building their first agent is as valuable as building it yourself.
• Autonomous Builder: You are a self-starter who thrives in an agile environment. You don't need excessive hand-holding to figure out how to host a script or deploy a small internal app.
• Relentless Tinkerer: You were the first person in your current role to start building agents. You have had personal subscriptions to Claude, Gemini, ChatGPT, Cursor, etc.

What You Bring:

• Experience building custom apps or integrations specifically for Freshdesk (or similar enterprise ticketing systems like Zendesk/Jira).
• Background working in or building tools for Customer Support, Customer Success, or Onboarding teams.
• Experience integrating with knowledge base platforms (like Gitbook or similar documentation sites).
• Proven experience taking automation projects from concept to deployment within a large, complex organization.
• Experience building internal platforms or developer tools that other teams use to self-serve.
• Experience with monitoring and observability for AI systems in production.