Job Description
Commotion builds an AI operating system for large enterprises, and at the centre of it is a context graph: a live model of an organisation’s entities, the relationships between them, and the decisions taken across them, assembled from the systems that already run the business.
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nMost enterprise AI stops at retrieval over documents. We think the layer that matters next is structural — resolved entities, explicit relationships, and a record of every decision and the reasoning behind it. A dashboard tells you what happened. A context graph lets an agent work out what to do about it, and show its working.
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nWe build this inside our clients’ own environments, where the data is real, messy and consequential. We are backed by Tata Communications.
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nAbout this role
nMost of this job is finding signal in data that is dirty, incomplete and contradictory, and then building models that hold up on it. The modelling is the smaller half. If you are looking for a role that is mostly about large language models, this is not it.
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nWhat you’ll do
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- Translate a client’s business problem into a machine learning problem. Sit with the people who own the outcome, work out what decision is being made and what it costs to get wrong, then decide what the model predicts, what the label is, and how the prediction reaches the decision. Say so when the answer is not a model at all.n
- Set the business metric and the model metric separately, and keep them connected. A lift in AUC that moves nothing operationally is a failed project here.n
- Profile the data before modelling it. Work out what is trustworthy, what is abandoned, what is quietly wrong. Establish a baseline before proposing anything learned.n
- Engineer features from warehouse and graph data, and keep them consistent between training and serving.n
- Build the models: classification and regression, propensity and churn, lookalike and audience expansion, ranking and recommendation, unsupervise

📌 Senior Machine Learning Engineer (Ghaziabad)
🏢 Commotion
📍 Ghaziabad