Make eeze smarter and safer: retrieval, grounding and guardrails for an AI that negotiates with real money on the line.

eeze is an AI checkout. It answers buyer questions from a company's own docs, handles objections and can negotiate, but only inside rules the founder wrote, with every offer validated server-side. That combination of useful and safe is the whole product, and it is an engineering problem more than a prompt problem.

You will own the AI layer: retrieval quality, grounding and citations, evaluation, guardrails and cost. You will also help clients ship AI features in their own products during consulting engagements.

What you will do

Improve retrieval and grounding so answers cite the tenant's knowledge base accurately

Design and enforce guardrails around offers, discounts and claims

Build evaluation suites that catch regressions before customers see them

Tune latency and cost across Anthropic and OpenAI models, including bring-your-own-key setups

Turn knowledge-gap analytics into a product feature founders act on

Prototype and ship AI features with clients during engagements

Stay current on model capabilities and separate the useful from the noise

What we are looking for

Robust software engineering fundamentals; this is a production engineering role, not a research role





Hands-on experience shipping LLM features to real users

Practical knowledge of retrieval, embeddings, context management and evaluation

Experience with at least one of the major model APIs in production

A healthy scepticism and the habit of measuring model behaviour instead of assuming it

Able to work from our Toronto office part of the week

Nice to have

Experience with structured outputs, tool use and multi-step agent flows

Background in payments, commerce or other domains where mistakes cost money

Familiarity with guardrail and validation patterns for user-facing AI

Experience running evals in CI

Who you are

You treat the model as a component, not a magic box

You would rather ship a reliable small feature than demo an impressive fragile one

You test with adversarial inputs because customers will

You write down what you learn so the team compounds

You care whether the answer is actually true

How we hire

1 Intro call with an engineer, about 30 minutes

2 Short take-home assignment around retrieval and grounding on real docs

3 Technical conversation about your assignment and AI features you have shipped

4 Conversation with the founders

5 Offer

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📌 AI Engineer (Ontario)
🏢 CLR3
📍 Ontario