Machine Learning Engineer in Bangalore, भारत

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, and 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; they own how models get built and improved; they own how models get served in the live https://jobeax.com/link/K1FKA7JWxGEqUAal : - 3+ 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, and distillation.- Familiarity with MLOps tooling, experiment tracking, model registries, and 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. (ref:hirist.tech)