Machine Learning Engineer

We are looking for a highly skilled Machine Learning Engineer - Model Development to build and scale our proprietary AI and machine learning capabilities that power intelligent credit decisioning and debt resolution. In this role, you will own the complete machine learning lifecycle from designing data pipelines and engineering features to training, fine-tuning, deploying, and continuously improving production-grade ML models.

Responsibilities:

• Build and train proprietary models on Oolka's data for 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.