Agent Engineer

Recent work by engineers on this team, all on live debt collection agents:

Build an intent identification layer for a voice agent, so every response is grounded in what the caller actually asked rather than what the model assumes, sharply reducing hallucinations on live calls

Extend the negotiation flow so the agent offers payment plans only within the limits the client has set, with guardrails that make out-of-bounds offers impossible rather than just unlikely

Grow the eval suite that replays real collection calls and verifies every legally required disclosure was delivered, word for word, before any release ships

Python with FastAPI, PostgreSQL, Temporal for background workflows, WebSockets holding real-time conversations open, and STT and TTS providers on the speech side, with LLMs via OpenAI and similar underneath.

Everything runs on Kubernetes with ArgoCD-driven GitOps deployments, SigNoz for observability, DeepEval driving the eval suite our CI runs before anything reaches production, Ory and OpenFGA for identity and access management, and a lot of beautifully built internal agent architecture underneath.

2 to 5 years building and running production systems. You've been on call for something you built and debugged it under pressure.

You write Python another person can read, review others' work thoughtfully, and debug by forming a theory and testing it, not by changing things until they work. Reading code you didn't write and working out what it does is the single most important skill in the role.

You've worked with async code and a relational database.

You're comfortable in front of a client: take vague feedback, ask the right questions, leave with a scoped change.

Strong written communication. Agent instructions, client explanations, and incident writeups are all writing, and an agent is only as precise as the instructions behind it.

LLM systems in production: eval frameworks, agent tooling, RAG pipelines, structured prompting

Conversational AI experience, voice or chat: dialogue design, IVR systems, chatbots, speech interfaces

A regulated industry: finance, healthcare, insurance, collections

Real-time or telephony systems: WebSockets, streaming audio, Twilio or similar

Founder or founding engineer experience

Explicitly not required: prior "AI engineering" as a job title, or a computer science degree. Careful engineers who own outcomes pick this up fast.

Small teams per client, with real ownership. You'll have peers to pair with and review your work, and you'll review theirs. We review all code, we write things down, and we'd rather hear an honest "I don't know yet" than a confident wrong answer.

Trust: We do the right thing, especially when nobody is checking. Clients hand us regulated conversations with their own customers, and we earn that every day.

Transparency: We write things down, share the real numbers, and say "I don't know yet" out loud, with each other and with clients.

Technically best solution: We choose what's right, not what's easiest or trendiest. When we notice we got it wrong, we fix it.

Decisiveness: We decide quickly with the information we have, commit, and correct course fast when reality disagrees.

Simplicity: We keep systems, processes, and words simple. Complexity is a cost we pay only when it clearly buys something.