ML Engineer – BFSI Domain

• Build robust ML pipelines and automate model training, evaluation, and deployment.
• Optimize and tune models for financial time-series, pricing engines, and fraud detection.
• Collaborate with data scientists and data engineers to deploy scalable and secure ML models.
• Monitor model drift, data drift, and ensure models are retrained and updated as per regulatory norms.
• Implement CI/CD for ML and integrate with enterprise applications.
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• 3–5 years in ML engineering.
• Hands-on experience with MLOps and ML pipeline automation in BFSI context.
• Knowledge of security and compliance in ML lifecycle for finance.

Tech Stack

• Languages: Python
• ML Platforms: MLflow, Kubeflow
• MLOps Tools: Airflow, MLReef, Seldon
• Libraries: scikit-learn, XGBoost, LightGBM
• Cloud: GCP AI Platform
• Containerization: Docker, Kubernetes

• 3–5 years in ML engineering.