ML Engineer

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.