Applied AI Engineer

About Prodigal

Prodigal is the connected AI platform leading financial institutions use to run their operations.

We work with banks, lenders, credit unions, and other financial companies that lend money to people and manage those relationships over time.

These institutions make millions of high-stakes decisions every day. Who should they reach? When should they reach them? What should they say or offer? When should a case move to a human? How should that change based on the borrower, the account, previous interactions, and the regulations involved?

Getting those decisions right requires a deep understanding of the people, processes, rules, and edge cases behind them.

Prodigal has spent the last eight years building that understanding. More than a billion interactions between financial institutions and their customers have shaped the intelligence, guardrails, and AI agents we now run in production across North America.

Today, our AI agents analyze conversations, capture context, guide human agents, decide the next action, conduct customer conversations, orchestrate outreach, and help people complete payments and resolutions. They are connected, so what is learned in one interaction can inform what happens next.

We are expanding this swarm of AI agents across more of the work financial institutions do: originations, document processing, back-office workflows, servicing, and other critical operations where money, identity, people, and regulation intersect.

We are backed by Y Combinator, Accel, and Menlo Ventures, and work with 100+ financial institutions across North America.

About the Role

We're looking for an AI Engineer to join the proAgent team and work on the systems that power our autonomous voice agents in live financial conversations. You'll work alongside a small, high-agency team where you will have real ownership over features and components from day one.

This is a hands-on engineering role. You'll be deep in the code - building agent logic, integrating speech models, tuning prompts, and debugging gnarly real-time issues. You don't need to have done all of this before, but you need to be the kind of engineer who figures things out fast, takes feedback well, and ships.

What You'll Do ( TL;DR version )

Agent Loop & Reasoning

Build and maintain components of the agentic runtime that powers multi-turn financial conversations - covering payment negotiations, compliance guardrails, and objection handling

Implement context management logic that tracks consumer state, conversation history, and business rules across long, branching dialogues

Write and iterate on primitives that balance conversational fluidity with structured reasoning - the agent needs to feel human while making verifiable decisions

Voice AI Systems

Work on our real-time voice pipeline with a target of sub-1s latency across transcription, reasoning, and synthesis

Contribute to VAD tuning and turn-taking logic that makes conversations feel natural

Help evaluate and integrate speech models (STT, TTS, speech-to-speech) - we currently work with ElevenLabs and Cartesia for TTS, and Deepgram for STT, and are always exploring what's next

Debug streaming audio and WebRTC issues in production

LLM Infrastructure

Write and refine prompts, implement orchestration flows, and contribute to model routing logic

Build components of our evaluation framework - helping measure agent quality across conversation quality, empathy, and compliance adherence

Stay curious about new models and tools - flag opportunities and contribute to build-vs-integrate discussions

What You Bring

2 – 5 years of hands-on engineering experience, with good exposure to building or working with ML or AI systems in production.

Solid Python fundamentals - you are comfortable writing clean, maintainable code and debugging production issues.

Some experience working with LLMs: prompt engineering, API integrations, or building simple pipelines or agents.

High bias for action - you don't wait to be told exactly what to do, and you push yourself to ship rather than over-engineer.

Strong fundamentals in Python; familiarity with TypeScript is a plus.

Eager to learn in a fast-moving environment, take ownership of your work, and ask good questions.

Even Better

Exposure to voice AI: speech recognition, synthesis, or telephony systems - even if only through personal projects or coursework

Any background or interest in fintech, lending, or collections

You've tinkered with Twilio, LiveKit, ElevenLabs, or similar real-time infrastructure

You've built a small agentic system - even a side project - that combines LLM reasoning with structured actions

Familiarity with streaming protocols, WebRTC, or low-latency system design

Why This Role

Real ownership from day one: The Applied AI team behind proAgent is small in size, high in talent density. You'll own components, ship features, and see your work in live consumer conversations within weeks.

Frontier work: voice AI that reasons, decides, and acts is one of the hardest problems in applied AI. You'll be learning by doing on problems most engineers never touch.

Strong mentorship: you'll work directly with senior engineers and the AI Lead who will invest in your growth - this is a place to level up fast.

High leverage early career: the decisions you make and the code you write will impact millions of financial conversations. Rare for an early-career role.

From day 1, Prodigal has been defined by talented, humble, and hungry leaders and we want this mindset and culture to continue to blossom from top to bottom in the company. If you have an entrepreneurial spirit and want to work in a fast-paced, intellectually-stimulating environment where you will be pushed to grow, then please reach out because we are looking to build a transformational company that reinvents one of the biggest industries in the US.

To learn more about us - please visit the following:

Our Story - https://www.prodigaltech.com/our-story

What shapes our thinking - https://link.prodigaltech.com/our-thesis

Our website - https://www.prodigaltech.com/