- 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 company'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 company's 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.
Apply with:
- Your resume highlighting relevant ML/modeling experience
- Brief note on what excites you about this chance
- Links to relevant projects, papers, or GitHub repositories (optional)