AI Engineer

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

• Strong 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