Senior Machine Learning Engineer - AI

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.

Most enterprise AI stops at retrieval over documents. We build this inside our clients’ own environments, where the data is real, messy and consequential. We are backed by Tata Communications.

Most of this job is finding signal in data that is dirty, incomplete and contradictory, and then building models that hold up on it. If you are looking for a role that is mostly about large language models, this is not it.

Translate a client’s business problem into a machine learning problem. A lift in AUC that moves nothing operationally is a failed project here.

Profile the data before modelling it. Engineer features from warehouse and graph data, and keep them consistent between training and serving.

Apply graph algorithms where structure is the signal: community detection, centrality, link prediction, node embeddings.

Deploy, then own what follows: retraining cadence, validation, champion and challenger, drift monitoring, and the call on when a model gets pulled.

Set the bar for how modelling work is done here, and bring newer engineers up to it.

4 to 9 years shipping models into production and owning them after launch.

• You can take an ambiguous business ask and frame it as an ML problem: the decision, the objective, the label, the unit of prediction, the evaluation, and the fallback when the model is not confident. Deep applied statistics and data analysis. Strong SQL, Python and Spark at volume.
• You can explain why a model that looks strong offline fails in production.
• The judgment to tell a data problem from a model problem, and to say when the data cannot support the question.
• Entity resolution or record linkage in production using an existing library or MDM tool: blocking strategies, probabilistic and ML-based matching, threshold design, clustering, and the human review loop. Every engineer here works with Claude and agentic coding tools daily, for exploration, transformation code, test data and analysis scaffolding. Expect travel, and expect to sit with the client’s own data owners.

Record linkage, deduplication or data reconciliation.

Domain exposure in manufacturing, FMCG, insurance or banking.