Machine Learning Engineer

Role overview

Experience and core requirements

• Strong hands-on Python for machine learning and production-quality software development is required.
• Knowledge of supervised and unsupervised learning, feature engineering, model selection, cross-validation, and performance metrics is required.
• Practical experience with scikit-learn, XGBoost, LightGBM, pandas, and NumPy is requested; deep learning frameworks are relevant where applicable.
• Good SQL skills and experience with large, complex datasets are required.
• Production model experience should include packaging, APIs, batch inference, containers, and automated pipelines.

Cloud, development, and governance skills

• Hands-on AWS knowledge is required; preferred services include SageMaker, S3, ECR, ECS/EKS or Lambda, Step Functions, and CloudWatch.
• Git-based development, code reviews, automated testing, and CI/CD fundamentals are required.
• Knowledge of model monitoring, data/model drift, explainability, reproducibility, and model lifecycle governance is required.
• Clear communication of technical findings to technical and business stakeholders is required.

Work scope

• Deliver end-to-end machine learning use cases and deploy models into production.
• Develop, evaluate, and productionize machine learning models using Python, data science methods, and AWS services.
• Apply software development, testing, CI/CD, monitoring, and model governance practices throughout the model lifecycle.
• Communicate technical findings to technical and business stakeholders.

Preferred background

• Banking, financial services, or other regulated-industry experience is preferred.
• Understanding of model governance, validation, and documentation expectations in controlled environments is advantageous.