ML Engineer
Bengaluru, Karnataka, IndiaJobNo compensation foundPosted 1mo agoVerified open 2 days ago
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At a glance
Compensation
No compensation found
Location
Bengaluru, Karnataka, India
Work Authorization
Not specified
Job overview
The ML Engineer will build and train proprietary models for repayment scoring, negotiation outcome prediction, and credit‑risk signals, managing the full model lifecycle and ensuring quality in production.
Skills & qualifications
RequiredNice to have
Skills
Artificial IntelligenceData EngineeringAWS SageMakerFeature ExtractionTensorFlowRankingCI/CDMachine LearningFraudMLFLowPyTorchA/B TestingFeature EngineeringData PipelinesModel ServingMLOpsKubeflowSageMakerTritonTorchServeTensorFlow ServingBias MitigationFairnessExplainabilityCredit RiskFraud Detection
Qualifications
3+ Years Building and Shipping ML Models in Production
Full job description
Responsibilities:
- 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, 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 you own; they own how models get built and improved; they own how models get served in the live product.
Requirements:
- 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.
You've read the whole posting — now see how you match it.