Machine Learning Engineer

Job Summary

We are seeking an experienced Machine Learning / AI Engineer to join a dynamic and multicultural team within a leading global financial institution at the forefront of digital transformation and innovation. This role involves designing, building, and deploying end-to-end machine learning solutions across international and cross-functional projects supporting strategic business functions across banking, finance, and risk domains. The ideal candidate will be hands-on in both model development and engineering scalable, production-grade ML systems, while ensuring alignment with AI governance and compliance standards. This is a 12 months contract hiring.

Mandatory Skill-set

• Master’s degree in AI, Machine Learning, or Data Science;
• Must have 6+ years in data science and ML, including 3+ years in ML engineering lifecycle;
• Proficiency in Python and ML libraries (pandas, scikit-learn, TensorFlow/PyTorch);
• Must have experience with NoSQL databases; exposure to graph databases is a plus;
• Familiarity with MLOps tools such has MLflow, TFX, Airflow, Kubeflow;
• Cloud deployment experience such as AWS, GCP, or Azure;
• Strong grasp of supervised/unsupervised learning, NLP, time series analysis;
• CI/CD pipeline and containerization expertise (Docker, Kubernetes);
• Understanding of AI governance, model risk, and regulatory compliance and the ability to explain technical concepts to non-technical stakeholders;
• Proven experience in end-to-end ML lifecycle.

Desired Skill-set

• Knowledge of Responsible AI frameworks and fairness/bias testing;
• Experience with feature stores, model registries, and data versioning;
• Familiarity with data privacy, anonymization, and compliance in regulated sectors.

Responsibilities

• Collaborate with data scientists and business teams to define ML solutions and build PoCs;
• Deploy and maintain ML models using MLOps best practices;
• Build scalable data pipelines and monitor model performance;
• Ensure models comply with internal AI policies and audit standards;
• Support feature engineering and occasional model development;
• Automate model retraining, testing, and performance tracking;
• Document workflows, governance checkpoints, and risk assessments;
• Work closely with DevOps, IT, and security teams to integrate ML solutions into enterprise platforms.

Should you be interested in this career opportunity, please send in your updated resume to [email protected] at the earliest.

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