AI & Applied Research · Bengaluru, India
Machine Learning Engineer
Three active workstreams: price-anomaly detection over Northwind Commodities' trade blotter, document extraction for Orbit Supply covering roughly 30,000 pages a month of bills of lading and purchase orders, and retrieval over a contract library of 140,000 documents. Every one of them starts with an evaluation set, not a model. If you cannot measure it against a labelled sample the client agrees with, we do not ship it.
- Full-time — hybrid, three days a week on site
- 3-6 years shipping models other people depend on, rather than models that lived in a notebook
- Bengaluru, India
The work
What you will do
- Build and maintain evaluation sets before any modelling work starts, including the adversarial and long-tail cases the client keeps quiet about.
- Take the extraction pipeline from 91 to 97 per cent field-level accuracy across 14 field types, with a confidence threshold that routes the rest to human review.
- Serve models inside a 400 ms p95 budget, including retrieval, with a documented rollback to the previous version.
- Instrument drift and volume monitoring, and define the trigger conditions for retraining rather than retraining on a calendar.
- Explain to non-technical stakeholders exactly where the model will be wrong, before they find out in production.
You
What we look for
- Python and PyTorch, plus practical work with transformer models — fine-tuning, quantisation, or serving them behind an API you kept up.
- A real grasp of calibration, class imbalance and the difference between offline metrics and the decision the model actually drives.
- At least one model deployed behind live traffic, with the monitoring and rollback path you built for it.
- Healthy scepticism about published benchmark numbers, and a habit of reproducing them before quoting them.
- The judgement to say a rules engine will beat a model on a particular problem, and to say it early.
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