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Machine Learning Engineer

India💼 Full-time🗓 2026-06-01 → 2026-07-28

Core

Build, maintain, and scale ML infrastructure end-to-end for credit underwriting, fraud detection, and credit scoring models serving real-time predictions at millions of requests per day.

Role type

Machine Learning Engineer (Production)

Builds

Core credit models, infrastructure, and tooling for real-time credit decisions and fraud signals.

Domain

Fintech / Credit Lending / Machine Learning

Deliverable

production ML models

Required skills

Python, XGBoost, scikit-learn, pandas, numpy, ML pipeline construction, feature engineering, model deployment, real-time prediction systems, drift monitoring, reject inference, deep generative approaches, LLMs/agentic AI, structured and unstructured data processing

Technologies

XGBoost, scikit-learn, pandas, numpy

Responsibilities

Build and scale ML infrastructure for feature computation and model training/deployment; implement observability and drift monitoring; evaluate selection bias in credit models; explore LLMs/agentic AI for feature generation and analysis; collaborate with underwriting and product teams to build signals for creditworthiness.

Seniority

Mid-level, hands-on IC

Rewrite
## About the Role Branch’s ML based underwriting models are core to our business and directly drive business value. Every credit decision, fraud signal, and credit scoring model we deploy has a direct impact on real customers’ access to capital. We are looking for engineers who want to grow with us, take deep ownership, and have a genuine impact on how ML is built and deployed in production. We are hiring ML engineers to join our ML teams in India. These teams own our core credit models, infrastructure, and the tooling that powers them. They also actively research and develop what comes next for ML at Branch for real credit and lending problems. ## Responsibilities - Build, maintain, and scale ML infrastructure end-to-end: feature computation pipelines serving real-time predictions at millions of requests per day, and training and deployment pipelines spanning classical and cutting-edge models. - Build observability and drift monitoring to detect and respond to model degradation in production. - Evaluate and address selection bias in credit models using techniques from reject inference to deep generative approaches. - Explore using LLMs/agentic AI to generate features from raw data, automate segment analysis, inform credit and policy exploration, and more. - Collaborate with credit underwriting, product, and backend teams to build signals from structured and unstructured data that identify creditworthy borrowers and power our underwriting models. ## Requirements - 2–5 years of hands-on ML engineering experience in production environments, not just research or notebooks. - Strong skills in building machine learning models using both structured and unstructured data. - Strong Python proficiency, including ML libraries (XGBoost, scikit-learn, pandas, numpy) and software engineering fundamentals. - Experience building or maintaining ML pipelines, training, evaluation, feature engineering, or model deployment. - Have a diverse range of data skills, including experimentat ## Nice to Have ## Benefits
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