Evismart - AI/ML Engineer
Requirements
• machine learning platforms in production • Depth in document and information extraction from unstructured, inconsistent text. Prescriptions are the core data object here and they are messy. • document and information extraction • You have built a system where user corrections became better models, and you can walk through exactly how the data moved. • user corrections became better models • Genuine evaluation rigour — you can define what "accurate" means for a given category, defend it, and ship against it. • evaluation rigour • Judgment about when not to use a model. A significant share of this problem is classification and mapping, and deterministic rules beat ML for a lot of it. • What success looks like • By 90 days — you can tell us, with evidence, where our models actually fail and for which customers. The squads agree on a single evaluation standard.
Responsibilities
• Establish measurement. Build per-model, per-customer, per-category accuracy reporting that Product and Sales can act on. Nothing else you do matters until this exists. • Establish measurement. • Build the correction flywheel. Capture the quality-control corrections our customers already make, turn them into structured, versioned training data, and close the loop with human review where confidence is low. • Build the correction flywheel. • Standardize the squads. One deployment path, one evaluation harness, one model and dataset registry across restoration design, scan quality, prescription extraction and agents. • Standardize the squads. • Solve multi-tenant model behaviour. Thousands of customer-specific product codes, prescription conventions and unwritten preferences sitting over a shared model base — without building a bespoke model per customer. • Solve multi-tenant model behaviour. • Take inference to production scale. Move workloads from on-premise to cloud with understood cost per inference and real concurrency headroom. • Take inference to production scale. • • Set technical direction for AI across the company, partnering closely with the CTO, Product and the operations teams in Manila who currently absorb the work our models can't yet do. • Set technical direction for AI • full-time in-office role at our Vancouver office, five days a week. • What we're looking forRequired • By six months — customer corrections flow into training data automatically. At least one production model has measurably improved through that loop. • By six months • By twelve months — prescription quality control runs with confidence thresholds, routing only genuinely ambiguous cases to human review. We can state our accuracy publicly and defend it in a sales conversation. • By twelve months
Benefits
• Larger competitors have bigger AI teams and a five-year head start on modelling. We are not trying to beat them at that. • We are trying to build something they structurally cannot: a system that gets better every time one of six thousand laboratories tells us we got something wrong. That data exists. The loop does not. • If you have built this kind of flywheel before — or you have been close enough to one to know exactly why most of them fail — we should talk.
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