Machine Learning Engineer

Years of Experience *

Minimum 6-7 Years Of Relevant Experience

Required Skills *

• Strong hands-on Python for machine learning and production-quality software development.
• Solid knowledge of supervised and unsupervised learning, feature engineering, model selection, cross-validation and performance metrics.
• Practical experience with common ML libraries such as scikit-learn, XGBoost, LightGBM, pandas and NumPy; deep learning frameworks where relevant.
• Good SQL skills and experience working with large, complex datasets.
• Experience productionizing models, including packaging, APIs, batch inference, containers and automated pipelines.
• Hands-on AWS knowledge, preferably including SageMaker, S3, ECR, ECS/EKS or Lambda, Step Functions and CloudWatch.
• Experience with Git-based development, code reviews, automated testing and CI/CD fundamentals.
• Understanding of model monitoring, data/model drift, explainability, reproducibility and model lifecycle governance.
• Ability to communicate technical findings clearly to both technical and business stakeholders.

Required Experience / Knowledge Areas *

• 6-7+ years across Data Science, Machine Learning Engineering or applied ML roles, with evidence of models deployed into production.
• Experience delivering end-to-end ML use cases rather than working only on infrastructure or platform administration.
• Experience in banking, financial services or another regulated industry is preferred.
• Understanding of model governance, validation and documentation expectations in controlled environments is advantageous.