Overview

You will work on neural network models that read clinical documentation and predict medical codes.

Responsibilities

• Own diagnosis of model errors on clinical text: find where predictions go wrong and trace each pattern to its root cause.
• Design experiments that test one specific fix at a time, and decide from the results whether the fix moves forward.
• Train, fine-tune, and evaluate neural network models for multilabel classification on clinical documentation.
• Improve precision on imbalanced classes, where a wrong code has a real cost.
• Write production Python that moves proven fixes into the automated coding pipeline.
• Explain the tradeoffs of a proposed change to the AI Team Lead before it touches a production model.
• Document every experiment: what you tried, what happened, and what you decided.

Qualifications

• At least 1 year of hands-on machine learning work outside coursework.
• Hands-on experience fine-tuning pretrained language models for text classification.
• Project work training deep learning models in Python.
• Experience with multilabel classification, including handling class imbalance.
• Production Python: code that runs inside a larger system, beyond notebook experiments.
• Strong debugging and root-cause skills: you can work out why a model or pipeline is behaving unexpectedly.
• Willing to act on partial evidence instead of waiting for exhaustive experiments.
• Explains tradeoffs clearly before making a change.
• Methodical and careful, because this work affects real claims.

Nice to have

• Making model predictions reliable enough for automated decisions.
• Adapting language models to specialized vocabulary.
• Taking a model from research prototype to production deployment.
• Base salary of ₹20 to ₹30 lakh per year, commensurate with experience.
• Relocation assistance, reimbursed against receipts, up to a cap.

Benefits

• Health insurance, provident fund, and leave per XIPL policy.
• Company cab service for all Noida employees.