Forward Deployed Engineer
Forward Deployed Engineer
AviaryAI | Chicago, Illinois | In person
About the role
A forward deployed engineer at AviaryAI takes a signed contract and turns it into a live AI agent making real calls and sending real texts to a credit union's members. Then you make it better until it hits the numbers the client signed up for.
A forward deployed engineer at AviaryAI takes a signed contract and turns it into a live AI agent making real calls and sending real texts to a credit union's members. Then you make it better until it hits the numbers the client signed up for.
You sit between the client and the platform. You are the person who understands both what the credit union's VP of Operations is trying to accomplish and what the agent is actually doing on call 4,000. Most of the interesting problems live in the gap between those two things.
This is a technical role with a client in the room. It is not a client success role with a technical veneer, and it is not a software engineering role where someone else handles the customer.
What you will do
Build and launch agents. Configure the agent for each client's use case: the prompt, the call flow, the transfer and escalation logic, the guardrails, the SMS fallback. Test it against real scenarios before a single member hears it.
Own the integration surface. Work with the client's technical team to define the data spec, map fields from their core banking system, set up file delivery, and confirm that what comes back to them is what they need. When the file is malformed on a Tuesday morning, you are the one who finds out why.
Read the logs and fix what you find. When an agent goes silent, interrupts a member, or takes too long to answer, you diagnose it from the call logs and the data. You will run SQL against call health, isolate the failure, and either fix it or hand engineering a precise repro.
Own the outcome after launch. Launch is the midpoint, not the finish line. You align on success criteria before kickoff, then run optimization cycles on prompts, timing, and flow until the client's targets are met and hold.
Run the client relationship at the operator level. Weekly calls with the people who own the contact center and the outcomes. You explain what the agent did and why, in language that makes sense to someone who does not think about LLMs for a living.
Make the next deployment faster. Turn what you learn into templates, checklists, and documentation. The goal is that deployment twenty takes a fraction of the effort deployment one did.
Feed the product. You are closer to production than anyone building the platform. What you bring back changes what gets built.
What we are looking for
You are comfortable in the tools. JSON configs, SQL, reading application logs, understanding how data moves between systems through APIs and flat files. You do not have to be a software engineer. You do have to be the kind of person who opens the log file instead of filing a ticket.
You have shipped something real people used, and you owned what happened after. A deployment, an integration, an internal tool, a data pipeline. The point is that you have lived through the gap between "it works in testing" and "it works for the customer."
You can sit across from a credit union executive and hold the room. Explaining why the agent did what it did, what you are changing, and when they will see the difference. Clear and specific, without hiding behind jargon.
You write clearly. Specs, runbooks, client updates, bug reports for engineering. Half this job is making complexity legible to someone else.
Two things you do not need
You do not need financial services experience. A sharp person learns this market in a few weeks. If you have it, good. If you do not, it is not a mark against you.
You do not need conversational AI experience. Prompts and evals are learnable. Judgment about what a real member will do when the agent says something unexpected is the part that takes longer, and that comes from caring about the person on the other end of the call.
Nice to have
One to three years in implementation, solutions engineering, technical support, or data and operations roles where you touched production systems
Python or another scripting language
Contact center, telephony, or conversational AI experience
Experience with core banking systems or financial data