About the role
The candidate will have responsibilities across the following functions:
Model Development and Training:
- Build and train proprietary models on Oolka's data: repayment-likelihood scoring, negotiation-outcome prediction, and credit-risk signals.
- Own the full model lifecycle: data collection, feature engineering, training, validation, deployment, and monitoring.
- Fine-tune LLMs and smaller models for domain-specific tasks: structured extraction from credit reports, negotiation dialogue quality.
- Build and maintain the evaluation framework that catches model quality regressions before they ship.
- Build feature pipelines from credit bureau, transaction, and repayment data.
- Design and operate model serving: batching, quantisation, versioning, and rollback for models you own.
- Monitor for model drift, degradation, and bias in production, and own the retraining loop.
- Partner with the AI Engineering team; you own how models get built and improved; they own how models get served in the live product.
Requirements:
- 3-5 years building and shipping ML models in production, not just integrating third-party AI APIs.
- Hands-on experience training and fine-tuning models (PyTorch or TensorFlow), classical ML and/or LLM fine-tuning.
- Strong feature engineering and data pipeline experience on structured/tabular data.
- Experience with model serving frameworks (Triton, TorchServe, TensorFlow Serving) and inference optimisation: batching, quantisation, distillation.
- Familiarity with MLOps tooling: experiment tracking, model registries, CI/CD for models (MLflow, Kubeflow, SageMaker, or equivalent).
ML-Specific Expertise:
- Built and shipped models predicting real-world outcomes (risk, churn, ranking, or similar).
- Credit, lending, or fraud experience is a strong plus.
- Experience with offline and online model evaluation, held-out test sets, A/B testing, and shadow deployment.
- Understanding of LLM fine-tuning approaches (LoRA/PEFT) and when fine-tuning beats prompting.
- Comfortable with the bias, fairness, and explainability bar that comes with models touching credit decisions.
- Has debugged a model quality regression in production and traced it back to a data or training root cause.
Growth Path:
- Direct impact on credit-decision accuracy and negotiation outcomes for real users.
- Ownership of Oolka's proprietary model layer, the part of the product competitors can't just prompt-engineer their way to.
- Exposure to a full-stack agentic AI product built on India-specific credit data.
- Path to leading the ML/model platform as Oolka's data advantage compounds S.