Position: ML Engineer

Exp: 3+ Years

Working Mode: WFO, 5 Days

Location: Bangalore

Job Overview:

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 lifecyclefrom designing data pipelines and engineering features to training, fine-tuning, deploying, and continuously improving production-grade ML models.

What You'll Own:

Model Development & Training:

- Build and train proprietary models on our 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, quantization, 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.

Technical Requirements:

Must-Have Experience:

- 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 optimization: batching, quantization, 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, 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 our 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 our data advantage compounds

Interview Process:

- 1. Technical Assessment: Intro + ML/modeling-focused technical discussion (60 minutes)

- 2. Model & System Design: Feature engineering, training, and evaluation-design conversation (60 minutes)

- 3. Final Round: Cultural alignment and team interaction

Next Steps:

Ready to help millions of Indians build better financial futures through AI? We'd love to hear from you.