Job Description:

Key Responsibilities
•        Design, develop, and deploy scalable Machine Learning models for real-world business problems
•        Work with large-scale structured and unstructured datasets
•        Build and maintain end-to-end ML pipelines (data ingestion, feature engineering, model training, evaluation, and deployment)
•        Collaborate closely with Data Engineers, Data Scientists, and Product teams
•        Optimize model performance, accuracy, and efficiency
•        Implement MLOps best practices including monitoring, versioning, and retraining
•        Ensure compliance with security, governance, and regulatory standards
•        Participate in code reviews, design discussions, and technical documentation
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Required Skills & Qualifications
•        6–11 years of overall experience with strong focus on Machine Learning Engineering
•        Strong programming experience in Python
•        Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn
•        Experience with data processing frameworks (Spark, Pandas, NumPy)
•        Strong understanding of ML algorithms, statistics, and model evaluation techniques
•        Experience deploying models using REST APIs, Docker, Kubernetes
•        Solid knowledge of SQL and working with large databases
•        Experience with cloud platforms (AWS, Azure, or GCP)
•        Strong problem-solving and communication skills
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Nice to Have
•        Prior experience working with financial services or banking clients
•        Experience with NLP, Time Series, or Recommendation Systems
•        Exposure to CI/CD pipelines and automation tools
•        Knowledge of data governance and model risk management