AI Engineer

CLR3; Toronto; Hybrid; CAD 90,000 - 130,000; Full time

Job summary

CLR3 is seeking a production-focused software engineer to own the AI layer for retrieval, grounding, and guardrails in an AI that negotiates with real money. You will also help clients ship AI features in their products during engagements.

You will improve retrieval, grounding, and citations, design guardrails, and build evaluation suites while tuning latency and cost across model APIs. This is a hands-on, client-facing role based in Toronto.

Qualifications

• Strong software engineering fundamentals and production-grade mindset.
• Hands-on experience shipping LLM features to real users.
• Practical knowledge of retrieval, embeddings, context management and evaluation.
• Experience with at least one major model API in production.
• Healthy scepticism and dedication to measuring model behavior.

Responsibilities

• Improve retrieval and grounding so answers cite the knowledge base accurately.
• Design and enforce guardrails around offers, discounts and claims.
• Build evaluation suites to catch regressions before customers see them.
• Tune latency and cost across model APIs and bring-your-own-key setups.
• Turn knowledge-gap analytics into a product feature for founders.
• Prototype and ship AI features with clients during engagements.
• Stay current on model capabilities and separate useful from noisy outputs.

Skills

Software engineering

LLM features

Retrieval / grounding

Embeddings

Evaluation

Model APIs

Adversarial testing

Job description

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