About Role:

We are looking for an AI/ML Engineer to join our data & analytics team at Fintech Cloud.

The role involves building machine learning models on loan and collections data across our multi-brand lending portfolio including credit risk scoring, NPA/default prediction, collections prioritization, and customer behavior modeling. You will work closely with the data engineering and analytics teams and take models from experimentation through to production deployment.

Mandatory Requirements:

- 23 years of minimum hands-on experience in an AI/ML or Data Science role.
- Prior experience working in a fintech, NBFC, banking, or lending environment is a must.
- Solid proficiency in SQL complex joins, window functions, query optimization on large datasets.
- Strong proficiency in Python Pandas, NumPy, and standard data manipulation workflows.

AI/ML Skills & Experience

- Solid grounding in statistics and ML fundamentals: probability, hypothesis testing, feature engineering, bias-variance tradeoff.
- Hands-on experience with ML algorithms: Logistic Regression, Decision Trees, Random Forest, XGBoost/LightGBM, clustering.
- Experience with scikit-learn and at least one deep learning framework (TensorFlow or PyTorch) is a plus.
- Understanding of model evaluation metrics: AUC-ROC, precision/recall, F1, KS-statistic, population stability index (PSI) especially relevant for credit risk models.




- Experience handling imbalanced datasets and cold-start problems (common in NPA/default prediction).
- Working knowledge of model deployment Flask/FastAPI APIs, or deploying models on cloud platforms (GCP/AWS/Databricks).
- Familiarity with BigQuery or similar cloud data warehouses for feature extraction at scale.
- Working knowledge of cloud platforms AWS and/or GCP (compute, storage, basic deployment services like EC2/Cloud Run, S3/GCS).
- Basic software engineering knowledge — Git/version control, writing clean modular code, REST APIs, and working comfortably in a Linux environment.
- Exposure to MLOps basics — model versioning, monitoring drift, retraining pipelines.
- Bonus: exposure to NLP or LLM-based applications (chatbots, document extraction, GenAI use-cases).

Key Responsibilities

- Design, build, and validate ML models for credit risk scoring, NPA/DPD prediction, and collections optimization.
- Work with large-scale loan and transactional data across 30+ lending brands in BigQuery/Databricks.
- Partner with the data engineering team to build reliable feature pipelines for model training and scoring.
- Deploy models into production (Flask APIs / batch scoring pipelines) and monitor performance over time.
- Translate business problems from Risk, Collections, and Credit teams into ML solutions.
- Document model logic, assumptions, and performance for internal and audit/compliance review.

📌 Ai Ml Engineer (Noida)
🏢 Fintech Cloud
📍 Noida