Machine Learning Specialist

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 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 opportunity

• Links to relevant projects, papers, or GitHub repositories (optional)