Forward Deployed Engineer
Posted yesterday
The role in short
Embed with financial clients to configure, script, and deploy production AI voice agents, optimizing them for real-world performance and compliance.
Client Deployment & Success Lead (Forward Deployed Engineer)
AviaryAI | Chicago, Illinois | In person
About AviaryAI
AviaryAI builds voice agents for credit unions, banks, and insurers — real calls, real members, real compliance requirements. Backed by Y Combinator, growing fast, millions of calls made.
About the Role
A forward deployed engineer takes a signed contract and turns it into a live AI agent making real calls and texts, then improves it until it achieves the client's goals.
You sit between the client and the platform. When the platform doesn't do something a client needs yet, you don't wait on a ticket. You build the fix yourself, scoped tightly to that client. When the ask is bigger than one client, you scope the problem precisely and hand it to engineering instead of hacking around it.
This is a technical role that also manages the relationships, aligns the stakeholders, and earns the trust that makes the work possible.
What You Will Do
Own deployment outcomes. Configure the agent: prompts, call flow, transfers, guardrails, SMS fallback — launch it, and optimize until it hits target and holds.
Build what's missing. When a client's problem needs more than config, write and ship the script or small automation that closes the gap. Know the difference between a fix that's yours to build and a problem that needs to go to engineering.
Create Repeatable Deployment Systems: Improve onboarding frameworks, document best practices, and refine internal processes so implementations become faster, more predictable, and easier to scale.
Cross-Functional Problem Solving: Collaborate with Engineering to resolve technical issues and maintain production performance, translating system behavior into clear business implications and next steps.
Strengthen and Expand Client Partnerships: Identify opportunities for improvement and expanded use cases based on performance data and evolving needs.
Inform Product Through Real-World Insights: Surface structured feedback from production environments to engineering and product teams to improve reliability, usability, and overall impact.
Feed the product. You're closer to production than anyone building the platform. What you bring back changes what gets built.
What We're Looking For
Must-haves:
High-Agency Mindset: Takes initiative without waiting for direction. You proactively identify key outcomes, make a plan, and follow through to ensure measurable results.
Technically Fluent: Comfortable shipping small scripts or automations against real production data. Experience with prompting and evaluating AI outputs. And a functional understanding of data flows and how systems integrate.
Strong Communicator: Communicates effectively to both technical teams and executives by simplifying complexity without losing nuance.
Client Advocacy: Able to quickly build trust and navigate competing priorities. Proven ability to develop long-term relationships.
Adaptable Under Pressure: Skilled in resolving complex issues in dynamic, high-stakes environments.
Fast Learner: Quickly grasps new concepts, tools, and domains, accelerating the path to impact and staying ahead in evolving technical and business contexts.
Thrives in Fast-Paced Environments: You are energized by environments where priorities shift quickly, stakes are high, and your work makes a visible impact.
You don't need financial services or conversational AI experience — both are learnable fast. What matters more is judgment about how a real person reacts when things don't go as scripted.
Nice to haves:
1 - 3 years of experience in implementation, client success, operations, QA, or support.
Experience in fintech, insurance, or financial services
Experience in conversational AI, voice workflows, or contact center technology.
Why You’ll Love This Role
You get to shape AI systems that are helping millions of people navigate their financial lives.
You’ll be a part of a small, elite team where your contributions have a measurable impact on AviaryAI’s growth trajectory and direction.
Prep for this interview
2 questions likely to come up at AviaryAI, each matched to a line in this posting.
- 1Discovery and scoping
They wrote: “AviaryAI builds voice agents for credit unions, banks, and insurers — real calls, real members, real compliance requirements. Backed by Y Combinator, growing fast, millions of calls made.”
Tell me about a time the customer asked for one thing but needed another.
How to answer
Why they ask: Good FDEs spot when a request is a guess at a solution. They want proof you can redirect a customer without losing their trust.
- What they asked for, and the detail that made you doubt it
- How you tested the doubt (shadowed users, looked at logs, ran a quick prototype)
- How you made the case to the customer
- What shipped and what changed for them
Tip: Pick a story where you checked your hunch with data or users before pushing back.
- 2LLMs, RAG and agents
They wrote: “A forward deployed engineer takes a signed contract and turns it into a live AI agent making real calls and texts, then improves it until it achieves the client's goals.”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
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